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Fwd: [CSPlus] International Journal of Big Data Intelligence, Summary of 2014 articles
by Gustaf Neumann 22 Jan '15

22 Jan '15

-------- Weitergeleitete Nachricht -------- Betreff: [CSPlus] International Journal of Big Data Intelligence, Summary of 2014 articles Datum: Thu, 22 Jan 2015 18:56:48 +0800 Von: cfp(a)grid.chu.edu.tw An: neumann(a)wu-wien.ac.at ******************************************************************************************************** Apologies if you got multiple copies of this email. If you'd like to opt out of these announcements, information on how to unsubscribe is available at the bottom of this email. ******************************************************************************************************** === Apologies if you receive multiple copies of this message === International Journal of Big Data Intelligence ISSN: 2053-1397 (online); 2053-1389 (print) http://www.inderscience.com/ijbdi Dear Distinguished Colleagues, The International Journal of Big Data Intelligence (IJBDI) delighted to provide summary of articles that published in 2014. We would like to invite you to read the articles. ================================================================================================== Vol. 1, No. 1/2 pp. 3-17: Big data (lost) in the cloud pp. 18-35: Designing and implementing a cloud-hosted SaaS for data movement and sharing with SlapOS pp. 36-49: Multi-source streaming-based data accesses for MapReduce systems pp. 50-64: A new approach for accurate distributed cluster analysis for Big Data: competitive K-Means pp. 65-73: Peculiarities of numerical algorithms parallel implementation for exa-flops multicomputers pp. 74-88: Towards quality-of-service driven consistency for Big Data management pp. 89-102: D-CEP4CMA: a dynamic architecture for cloud performance monitoring and analysis via complex event processing pp. 103-113: An extended analytical study of Arabic sentiments pp. 114-126: Health big data analytics: current perspectives, challenges and potential solutions Vol. 1, No. 3 pp. 127-140: Migrating enterprise applications to the cloud: methodology and evaluation pp. 141-150: A parallel tag affinity computation for social tagging systems using MapReduce pp. 151-165: Innesto: a multi-attribute searchable consistent key/value store pp. 166-171: Anomaly digging approach based on massive RFID data in transportation logistics pp. 172-180: Current trends in predictive analytics of big data Vol. 1, No. 4 pp. 181-191: Intelligent big data analysis: a review pp. 192-204: Cloud computing for brain segmentation - a perspective from the technology and evaluations pp. 205-214: Provenance for business events PP. 215-229: Should infrastructure clouds be priced entirely on performance? An EC2 case study PP. 230-243: Total exchange routing on hierarchical dual-nets ================================================================================================== pp. 3-17: Title: Big data (lost) in the cloud Author: Beniamino Di Martino; Rocco Aversa; Giuseppina Cretella; Antonio Esposito; Joanna Kołodziej Abstract: The big data era poses a critically difficult challenge and striking development opportunities to high performance computing (HPC). The major problem is an efficient transformation of the massive data of various types into valuable information and meaningful knowledge. Computationally-effective HPC is required in a fast-increasing number of data-intensive domains. With its special features of self-service and pay-as-you-use, cloud computing (CC) offers suitable abstractions to manage the complexity of the analysis of large data in various scientific and engineering domains. This paper surveys briefly the most recent developments on CC support for solving the big data problems. It presents a comprehensive critical analysis of the existing solutions and shows the further possible directions of the research in this domain including new generation multi-datacentre cloud architectures for the storage and management of the huge big data streams. Keywords: distributed data centres; resource provisioning; big data workflow; cloud computing; high performance computing; MapReduce; Hadoop; data analysis; data curation; data storage; data management. DOI: 10.1504/IJBDI.2014.063840 ------------------------------ pp. 18-35: Title: Designing and implementing a cloud-hosted SaaS for data movement and sharing with SlapOS Author: Walid Saad; Heithem Abbes; Mohamed Jemni; Christophe Cérin Abstract: For over a decade, the data requirements of e-Science applications increase drastically with the emergence of data-intensive applications. Several tools and frameworks have been developed to manage and handle the big amount of data for the grid platforms. However, the use of these tools by the basic scientist and the grid computing community is not well adopted because of the complexity of the installation and configuration processes. Recently, an open source distributed operating system for clouds emerged, namely SlapOS. The aim of SlapOS is to hide the complexity of IT infrastructures and software deployments from users. In this work, we propose a cloud-hosted data grid using the SlapOS cloud. Through a software as a service (SaaS) solution, users can request and install automatically any data movement and sharing tools like Stork and Bitdew without any intervention of a system administrator. The entire solution is now running in production into the SlapOS cloud ! at Paris 13 University. Intensive experiments have been conducted on the Grid '5000 testbed to validate our approach. Keywords: data-intensive applications; big data management; software as a service; SaaS; Stork; Bitdew; SlapOS; grid tools; cloud federation; software integration; cloud computing; data movement; data sharing; open source; distributed operating system; grid computing. DOI: 10.1504/IJBDI.2014.063860 ------------------------------ pp. 36-49: Title: Multi-source streaming-based data accesses for MapReduce systems Author: Jiadong Wu; Bo Hong Abstract: The MapReduce programming model, along with its open-source implementation - Hadoop - has provided a cost effective solution for many data intensive applications. Hadoop stores data distributively and exploits data locality by assigning tasks to where data is stored. In many cases, however, accessing remote data (rack-local and off-rack) is inevitable. In this paper we are evaluating the possibility of improving the remote data accessing performance by streaming data from multiple available replicas. The proposed design consists of a circular buffer, a slice reader and an enhanced DataNode. Such system is capable of adapting to both the static performance variance caused by network topology as well as dynamic variance caused by congestion. Extensive experiments show that multisource streaming can significantly improve the throughput of remote data access and accelerate the related map tasks by 10%-20%. For systems with heterogenous network links, upto 4× speedup w! as observed. Keywords: MapReduce; multisource streaming; remote data access; network topology; congestion; big data. DOI: 10.1504/IJBDI.2014.063843 ------------------------------ pp. 50-64: Title: A new approach for accurate distributed cluster analysis for Big Data: competitive K-Means Author: Rui Máximo Esteves; Thomas Hacker; Chunming Rong Abstract: The tremendous growth in data volumes has created a need for new tools and algorithms to quickly analyse large datasets. Cluster analysis techniques, such as K-Means can be distributed across several machines. The accuracy of K-Means depends on the selection of seed centroids during initialisation. K-Means++ improves on the K-Means seeder, but suffers from problems when it is applied to large datasets. In this paper, we describe a new algorithm and a MapReduce implementation we developed that addresses these problems. We compared the performance with three existing algorithms and found that our algorithm improves cluster analysis accuracy and decreases variance. Our results show that our new algorithm produced a speedup of 76 ± 9 times compared with the serial K-Means++ and is as fast as the streaming K-Means. Our work provides a method to select a good initial seeding in less time, facilitating fast accurate cluster analysis over large datasets. Keywords: k-means clustering; k-means++; streaming k-means; MapReduce; distributed cluster analysis; big data; initial seeding. DOI: 10.1504/IJBDI.2014.063844 ------------------------------ pp. 65-73: Title: Peculiarities of numerical algorithms parallel implementation for exa-flops multicomputers Author: Victor E. Malyshkin Abstract: Peculiarities of numerical algorithms parallel implementation for exa-flops multicomputers were considered and the appropriate examples were given. The problems of such big data processing were analysed and also, a solution was suggested. Problems of parallel implementation of large scale numerical models on a rectangular mesh were demonstrated by the parallelisation of the particle-in-cell method (PIC). For similar problems solution, the fragmentation of big data and computations were suggested. Fragmentation automatically provides different useful properties of a target program including dynamic load balancing on the basis of fragments migration from overloaded into underloaded processor elements of a multicomputer. Keywords: numerical algorithms; big data processing; parallel programming; large scale numerical models; exa-flops computation; fragmentation; fragmented programs; dynamic load balancing; processes migration; modelling; particle-in-cell; PIC; multicomputers. DOI: 10.1504/IJBDI.2014.063837 ------------------------------ pp. 74-88: Title: Towards quality-of-service driven consistency for Big Data management Author: Álvaro García-Recuero; Sérgio Esteves; Luís Veiga Abstract: With the advent of Cloud Computing, Big Data management has become a fundamental challenge during the deployment and operation of distributed highly available and fault-tolerant storage systems such as the HBase extensible record-store. These systems can provide support for geo-replication, which comes with the issue of data consistency among distributed sites. In order to offer a best-in-class service to applications, one wants to maximise performance while minimising latency. In terms of data replication, that means incurring in as low latency as possible when moving data between distant data centres. Traditional consistency models introduce a significant problem for systems architects, which is specially important to note in cases where large amounts of data need to be replicated across wide-area networks. In such scenarios it might be suitable to use eventual consistency, and even though not always convenient, latency can be partly reduced and traded for consis! tency guarantees so that data-transfers do not impact performance. In contrast, this work proposes a broader range of data semantics for consistency while prioritising data at the cost of putting a minimum latency overhead on the rest of non-critical updates. Finally, we show how these semantics can help in finding an optimal data replication strategy for achieving just the required level of data consistency under low latency and a more efficient network bandwidth utilisation. Keywords: cloud storage; data consistency; replication; geo-replication; data storage; NoSQL; quality-of-service; QoS; big data management; data semantics; latency; network bandwidth. DOI: 10.1504/IJBDI.2014.063853 ------------------------------ pp. 89-102: Title: D-CEP4CMA: a dynamic architecture for cloud performance monitoring and analysis via complex event processing Author: Afef Mdhaffar; Riadh Ben Halima; Mohamed Jmaiel; Bernd Freisleben Abstract: This paper presents a dynamic monitoring and analysis architecture for Cloud computing environments. It collects and analyses Cloud parameters to detect performance degradations. The proposed dynamic architecture, called D-CEP4CMA, is based on the methodology of complex event processing (CEP). It is designed to dynamically switch between different centralised and distributed CEP architectures depending on the load/memory of the CEP machine and network traffic conditions in the observed cloud environment. Experimental results demonstrate the efficiency of D-CEP4CMA and its performance in terms of precision and recall in comparison to centralised and distributed CEP architectures. Keywords: cloud computing; complex event processing; CEP; performance analysis; dynamic architecture; big data intelligence; cloud performance; performance monitoring. DOI: 10.1504/IJBDI.2014.063842 ------------------------------ pp. 103-113: Title: An extended analytical study of Arabic sentiments Author: Nawaf A. Abdulla; Mahmoud Al-Ayyoub; Mohammed Naji Al-Kabi Abstract: Due to the evolution of Web 2.0 technology, internet users are more capable of posting their comments and reviews to express their opinions and feelings about everything. Hence, the necessity of automatically identifying the polarity (be it positive, negative, or neutral) of these comments arose and new interdisciplinary field called sentiment analysis (SA) emerged. Unluckily, many studies were conducted on the English language whereas those on the Arabic language are quite few. In addition, the publicly available datasets and testing tools for SA of Arabic text are rare. In this paper, a relatively large dataset of Arabic comments is manually collected and annotated. The source is one of the most widely used social networks in the Arab world, Yahoo!-Maktoob. A comprehensive analysis of this dataset is presented and two popular classifiers, support vector machine (SVM) and Naive Bayes (NB) are used for empirical experimentations. The results show that SVM outperfor! ms NB and achieves a 64% accuracy level. Keywords: social networking; document-level sentiment analysis; Arabic text analysis; opinion mining; Arabic comments; support vector machine; SVM; naive Bayes. DOI: 10.1504/IJBDI.2014.063845 ------------------------------ pp. 114-126: Title: Health big data analytics: current perspectives, challenges and potential solutions Author: Mu-Hsing Kuo; Tony Sahama; Andre W. Kushniruk; Elizabeth M. Borycki; Daniel K. Grunwell Abstract: Modern health information systems can generate several exabytes of patient data, the so called 'health big data', per year. Many health managers and experts believe that with the data, it is possible to easily discover useful knowledge to improve health policies, increase patient safety and eliminate redundancies and unnecessary costs. The objective of this paper is to discuss the characteristics of health big data as well as the challenges and solutions for health big data analytics (BDA) - the process of extracting knowledge from sets of health big data - and to design and evaluate a pipelined framework for use as a guideline/reference in health BDA. Keywords: healthcare technology; big data analytics; BDA; data mining; cloud computing; health information systems; patient data; health big data. DOI: 10.1504/IJBDI.2014.063835 ------------------------------ pp. 127-140: Title: Migrating enterprise applications to the cloud: methodology and evaluation Author: Steve Strauch; Vasilios Andrikopoulos; Dimka Karastoyanova; Frank Leymann; Nikolay Nachev; Albrecht Stäbler Abstract: Migrating existing on-premise applications to the cloud is a complex and multi-dimensional task and may require adapting the applications themselves significantly. For example, when considering the migration of the database layer of an application, which provides data persistence and manipulation capabilities, it is necessary to address aspects like differences in the granularity of interactions and data confidentiality, and to enable the interaction of the application with remote data sources. In this work, we present a methodology for application migration to the cloud that takes these aspects into account. In addition, we also introduce a tool for decision support, application refactoring and data migration that assists application developers in realising this methodology. We evaluate the proposed methodology and enabling tool using a case study in collaboration with an IT enterprise. Keywords: data migration; application migration; decision support; database layer; application refactoring; cloud computing; interaction granularity; data confidentiality; remote data sources. DOI: 10.1504/IJBDI.2014.066319 ------------------------------ pp. 141-150: Title: A parallel tag affinity computation for social tagging systems using MapReduce Author: Hyunwoo Kim; Taewhi Lee; Hyoung-Joo Kim Abstract: Tag affinity is the relationship between tags. It is a useful information for search and recommendation in social tagging systems. Tag affinity is measured by several types of tag cooccurrence frequency. The computation of tag affinity is a time-consuming task as the tagging information is accumulated. To alleviate this problem, we propose a parallel tag affinity computation method using MapReduce. We present MapReduce algorithms for computing three types of tag affinity measures: macro, micro, and bigram tag cooccurrence frequency. Our experimental results show that the proposed MapReduce-based approach not only significantly outperforms existing methods based on a relational database but also provides high scalability. To the best of our knowledge, this approach is the first tag affinity computation on MapReduce. Keywords: parallelisation; social tagging; MapReduce; Hadoop; parallel tag affinity; tag cooccurrence frequency; bigram; big data. DOI: 10.1504/IJBDI.2014.066322 ------------------------------ pp. 151-165: Title: Innesto: a multi-attribute searchable consistent key/value store Author: Mahdi Tayarani Najaran; Norman C. Hutchinson Abstract: Key/value data storage systems serve as the fundamental component of scalable cloud-based services. However, the scalability of existing key/value datastores comes at the cost of a narrow data access API with relaxed data consistency. We present Innesto, a distributed key/value datastore that provides search as part of its API. Search allows data items to be retrieved based on constraints on multiple different attributes. Innesto's strong consistency data model and its transactional interface bring much of the power of traditional relational databases to cloud-scale performance. Isolation between transactions can be performed using either traditional locks or using lock-free synchronisation based on clock vectors. Our evaluation of Innesto shows that it offers these extra features with competitive performance compared to an industrial key/value datastore such as Cassandra which offers an inferior feature set. Keywords: key/value stores; cloud storage; multiattribute search; consistency; scalability; one-round transaction; big data; cloud computing; data storage. DOI: 10.1504/IJBDI.2014.066323 ------------------------------ pp. 166-171: Title: Anomaly digging approach based on massive RFID data in transportation logistics Author: Xiaohua Cao; Xiejun Zhang Abstract: In modern transportation logistics, anomaly significantly lowers the efficiency of production and the quality of service. Massive RFID data is produced to record the states of materials in transportation logistics. The data is of multi-attribute, randomness and various dimensions so that it is difficult to find out anomalies from these data. A deviation-based clustering approach is proposed to dig anomalies. Firstly, the features of RFID data are discussed from multi-attribute perspectives including time, location, data, sequence and path. Next, against the randomness and various dimensions of state data, a clustering approach is presented to unify the dimensions of state data and dig anomalies from random state data. The results show that the proposed approach can efficiently find more than 91.2% of anomalies among transportation logistics. Keywords: anomaly digging; massive RFID data; radio frequency identification; deviation models; clustering; transport logistics; big data. DOI: 10.1504/IJBDI.2014.066324 ------------------------------ pp. 172-180: Title: Current trends in predictive analytics of big data Author: Tomasz Wiktor Wlodarczyk; Thomas J. Hacker Abstract: Predictive analytics is a driving force motivating considerable interest in big data. Although there is clear interest in big data, the adoption rate of analytical techniques fuelled by big data that can extract knowledge and value from these data is less well understood. In this paper, we present a quantitative analysis of trends in publications related to predictive analytics, predictive modelling, big data and data intensive computing. Our evaluation shows an increasing popularity of big data in scientific publications, with ten-fold increase in the last three years. Concomitantly, we find that predictive analytics are connected with this trend, with two-fold increase in the last three years, but also a seven-fold increase in the same period when used in context with big data. We also classify the main application domains for big data and predictive analytics. Contrary to popular belief that big data is focused primarily on social media and business intelligence! , our analysis found that almost half of scientific publications using predictive analytics were in healthcare, smart services, the internet of things, and weather and environment. Our results indicate the early adoption of big data-based analytics in these domains. Keywords: big data; data intensive computing; predictive analytics; predictive modelling; elemental data; time series. DOI: 10.1504/IJBDI.2014.066326 ------------------------------ pp. 181-191: Title: Intelligent big data analysis: a review Author: Chun-Wei Tsai; Ya-Lan Yang; Ming-Chao Chiang; Chu-Sing Yang Abstract: Big data analysis is definitely an urgent task for most information systems. Its importance and potentials can be found in many recent studies. They buzzed with this research issue because the data we collect and create are increasing at an unprecedented rate. Thus, the data analysis process has to be reconsidered. In this paper, we will first give a brief discussion of big data from different perspectives, such as size of data, characteristics of data, and source of data. Then, data mining and other information retrieval technologies for big data will be addressed. A brief review of other computational intelligence technologies for big data will also be given. Finally, open issues and future research trends using computational intelligence technologies are presented to show their potentials for big data. Keywords: big data analysis; computational intelligence; data mining; information retrieval; soft computing; intelligent data analysis; information systems. DOI: 10.1504/IJBDI.2014.066957 ------------------------------ pp. 192-204: Title: Cloud computing for brain segmentation - a perspective from the technology and evaluations Author: Victor Chang Abstract: This paper examines how cloud computing can be used in the area of brain segmentation with regard to satisfactory technical and user evaluations. It explains eleven APIs associated with each brain segment and the process of capturing data for each segment. Functionality and experiments associated with each API are discussed. Dancing is to capture data more easily. Results are used to explain why some segments are more active in dancing, with two evaluations undertaken. The first evaluation is the use of brain segmentations developed for medical cloud computing education (MCCE) and results confirm that cloud computing offers a 20% improvement in learning satisfaction. The second evaluation is focused on recapturing a lost skill. Results confirm that volunteers have their heartbeat, blood pressure, emotion, body coordination and vision at their peak. Benefits of using cloud brain segmentation technology are presented to illustrate positive impacts to healthcare infor! matics, education and cost reduction. Keywords: healthcare cloud; brain segmentation; cloud computing; medical cloud; computing education; dancing; learner satisfaction; heartbeat; blood pressure; emotions; body coordination; vision; healthcare informatics; healthcare education; cost reduction. DOI: 10.1504/IJBDI.2014.066954 ------------------------------ pp. 205-214: Title: Provenance for business events Author: Rafat Hammad; Ching-Seh Wu Abstract: In today's business environment, applications generate massive amounts of business data at various levels of granularity. During execution of business processes, a number of issues may occur, e.g., system failures, process failures, service failures, or human errors, that can result in the processes not executing as expected, and as a result not adhering to the required compliance concerns. Business provenance is an emerging concept which gives the flexibility to capture information required to address a specific compliance or performance goal. This paper discusses the importance of data provenance and presents a framework to capture, model, and persists provenance for business events data. We propose a method to model the business events in such a way that can be used for continuous compliance monitoring and for historical root cause analysis. We present a design of our proposed framework and its components along with a prototype implementation. Keywords: provenance management; data streams; real-time monitoring; distributed event-based systems; message dependence graph; root cause analysis; business data; business provenance; data provenance; compliance monitoring. DOI: 10.1504/IJBDI.2014.066956 ------------------------------ pp. 215-229: Title: Should infrastructure clouds be priced entirely on performance? An EC2 case study Author: John O'Loughlin; Lee Gillam Abstract: The increasing number of public clouds, the large and varied range of VMs they offer, and the provider specific terminology used for describing performance characteristics, makes price/performance comparisons difficult. Large performance variation of identically priced instances can lead to clouds being described as 'unreliable' and 'unpredictable'. In this paper, we suggest that instances might be considered mispriced with respect to their deliverable performance - even when provider supplied performance ratings are taken into account. We demonstrate how CPU model determines instance performance, show associations between instance classes and sets of CPU models, and determine class-to-model performance characteristics. We show that pricing based on CPU models may significantly reduce, but not eliminate, price/performance variation. We further show that CPU model distribution differs across different AZs and so it may be possible to obtain better price/performance ! in some AZs by determining proportions of models found per AZ. However, the resources obtained in an AZ are account dependent, displays random variation and is subject to abrupt change. Keywords: cloud computing; virtual machines; performance; pricing; probability; brokers; infrastructure clouds; public clouds; CPU models; price-performance variation. DOI: 10.1504/IJBDI.2014.066955 ------------------------------ pp. 230-243: Title: Total exchange routing on hierarchical dual-nets Author: Yamin Li; Wanming Chu Abstract: The hierarchical dual-net (HDN) is a newly proposed interconnection network for building extra large scale supercomputers. The HDN is constructed based on a symmetric product graph (called base network), such as three-dimensional torus and n-dimensional hypercubes. A k-level hierarchical dual-net, HDN(B, k, S), is obtained by applying k-time dual constructions on the base network B. S defines a supernode set that adjusts the scale of the system. The node degree of HDN(B, k, S) is d0 + k, where d0 is the node degree of the base network. The HDN is node and edge symmetric and can contain huge number of nodes with small node-degree and short diameter. The total exchange, or all-to-all personalised communication, is one of the most dense communication patterns and is at the heart of numerous applications and programming models in parallel computing. In this paper, we show that the total exchange routing can be done on HDN efficiently and extra large scale HDNs can be i! mplemented easily. Keywords: interconnection networks; total exchange routing; hierarchical dual nets; HDN; supercomputers; all-to-all personalised communication. DOI: 10.1504/IJBDI.2014.066958 ------------------------------ === About IJBDI === International Journal of Big Data Intelligence (IJBDI) is a peer reviewed journal publishing original and high-quality articles covering a wide range of topics in big data intelligence. The journal has a distinguished editorial board with extensive academic qualifications, ensuring high scientific standards. === Prospective Authors === The IJBDI invites you to consider submitting a manuscript for inclusion in this journal. Prospective authors are encouraged to submit an electronic version of original, unpublished manuscripts. Accepted papers of IJBDI will undergo language copyediting, typesetting, and reference validation in order to provide the highest publication quality. The average reviewing process is less than 10 weeks. No publication charges! === IJBDI Coverage === Topics of interest include: -The 5Vs of the data landscape: volume, variety, velocity, veracity, value -Big data science and foundations, analytics, visualisation and semantics -Software and tools for big data management -Security, privacy and legal issues specific to big data -Big data economy, QoS and business models -Intelligence and scientific discovery -Software, hardware and algorithm co-design, high-performance computing -Large-scale recommendation systems and graph analysis -Algorithmic, experimental, prototyping and implementation -Data-driven innovation, computational modelling and data integration -Data intensive computing theorems and technologies -Modelling, simulation and performance evaluation -Hardware and infrastructure, green data centres/environmental-friendly perspectives -Computing, scheduling and resource management for sustainability -Complex applications in areas where massive data is generated The journal welcomes comprehensive survey papers on timely topics. === Special Issue === Special Issue is an effective way to draw attention to specific topics. Experienced researchers and practitioners are welcome to propose, organize, and guest edit special section (3-4 papers) / issue (6-8 papers) around topics of their interest and expertise. Once you propose a Special Issue (SI), you will be the Lead Guest Editor of the Special Issue. We look forward to your stimulating proposals and working with you in ensuring the SI bright. We will be pleased to assist with all questions on the organization of a Special Issue to its publication. Enquiries and special issue proposals should be directed to the editor Prof. Robert Hsu at chh(a)chu.edu.tw === Editorial Board === Advisory Editors: Rajkumar Buyya (University of Melbourne) Wuchun Feng (Virginia Tech) Tarek El-Ghazawi (George Washington University) Sanjay Ranka (University of Florida) Geoffrey Fox (Indiana University) I-Ling Yen (University of Texas at Dallas) Kai Hwang (University of Southern California) Albert Zomaya (University of Sydney) Viktor Prasanna (University of Southern California) Philip S. Yu (University of Illinois at Chicago) Sartaj Sahni (University of Florida) Jeffrey Tsai (University of Illinois at Chicago) Associate Editors: Jemal Abawajy (Deakin University, Australia) Nik Bessis (University of Derby, UK) Irena Bojanova (University of Maryland University College, USA) Yeh-Ching Chung (National Tsing Hua University, Taiwan) Ernesto Damiani (Università degli Studi di Milano, Italy) Thomas J. Hacker (Purdue University, USA) Marcin Paprzycki (Systems Research Institute, Poland) Regional Editors: Pavan Balaji (Argonne National Laboratory, USA) Jinjun Chen (University of Technology, Sydney, Australia) Beniamino Di Martino (Seconda Universitá di Napoli, Italy) Bhekisipho Twala (University of Johannesburg, South Africa) Cho-Li Wang (The University of Hong Kong, Hong Kong SAR, China) Editorial Board: Bernady Apduhan (Kyushu Sangyo University, Japan) Viraj Bhat (Yahoo, USA) Jian-Nong Cao (Hong Kong Polytechnic University, Hong Kong SAR, China) Christophe Cerin (University of Paris 13, France) Yuri Demchenko (University of Amsterdam, Netherlands) Bin Guo (Northwestern Polytechnical University, China) Hung-Chang Hsiao (National Cheng Kung University, Taiwan) Runhe Huang (Hosei University, Japan) Patrick Hung (University of Ontario Institute of Technology, Canada) Bahman Javadi (University of Western Sydney, Australia) Hai Jiang (Arkansas State University, USA) Hai Jin (Huazhong University of Science and Technology, China) Alex Mu-Hsing kuo (University of Victoria, Canada) Che-Rung Lee (National Tsing Hua University, Taiwan) Hui Lei (IBM T. J. Watson Research Center, USA) Victor Leung (The University of British Columbia, Canada) Keqin Li (State University of New York at New Paltz, USA) Keqiu Li (Dalian University of Technology, China) Qingwei Li (University of South Florida, USA) Xiaoming li (University of Delaware, USA) Chun-Yuan Lin (Chang Gung University, Taiwan) Shiyong Lu (Wayne State University, USA) Jianhua Ma (Hosei University, Japan) Prabhat K. Mahanti (University of New Brunswick, Canada) Victor Malyskin (Institute of Computational Mathematics and Mathematical Geophysics, RAS, Russian Federation) Onur Mutlu (Carnegie Mellon University, USA) Yonghong Peng (University of Bradford, UK) Pit Pichappan (Al-Imam Muhammad Ibn Saud University, Saudi Arabia) Seungmin Rho (Sungkyul University, Republic of Korea) Frode Eika Sandnes (Oslo and Akershus University College of Applied Sciences, Norway) Luis Veiga (Instituto Superior Técnico and INESC-ID Lisboa, Portugal) Monica Wachowicz (University of New Brunswick, Canada) Honggang Wang (University of Massachusetts Dartmouth, USA) Shangguang Wang (Beijing University of Posts and Telecommunications, China) Yufeng Wang (Nanjing University of Posts and Telecommunications, China) Tomasz Wiktor Wlodarczyk (University of Stavanger, Norway) Jinsong Wu (Alcatel-Lucent, China) Feng Xia (Dalian University of Technology, China) Yang Xiang (Deakin University, Australia) Chu-Sing Yang (National Cheng Kung University, Taiwan) Laurence T. Yang (St Francis Xavier University, Canada) Neil Y. Yen (The University of Aizu, Japan) Shui Yu (Deakin University, Australia) Zhiwen Yu (Northwestern Polytechnical University, China) Daqiang Zhang (Tongji University, China) Jia Zhang (Carnegie Mellon University, USA) Hong Zhu (Oxford Brookes University, UK) Kind regards, Robert Hsu, Editor-in-Chief International Journal of Big Data Intelligence http://www.inderscience.com/ijbdi ******************************************************************************************************** The CSPlus (Computational Science Publicity and Liaison for Ubiquitous Society) mailing list has been setup to share information with respect to upcoming CS related events. If you feel that the number of such emails is too many, filter them to less-frequently-read folders in your email client. If you do not wish to receive any emails from CSPlus, you can unsubscribe from the mailing list (http://grid.chu.edu.tw/mailling_list/index.php) or use the following link http://grid.chu.edu.tw/unsubscribe.php?mail=neumann@wu-wien.ac.at If you need any help, please email us at cfp-admin(a)grid.chu.edu.tw ********************************************************************************************************

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Fwd: [AISWorld] ETHICOMP 2015: call for abstracts, submission deadline extended to 23 February 2015
by Gustaf Neumann 22 Jan '15

22 Jan '15

-------- Weitergeleitete Nachricht -------- Betreff: [AISWorld] ETHICOMP 2015: call for abstracts, submission deadline extended to 23 February 2015 Datum: Thu, 22 Jan 2015 09:20:28 +0000 Von: Bernd Stahl <bstahl(a)dmu.ac.uk> An: 'AISWorld(a)lists.aisnet.org' <AISWorld(a)lists.aisnet.org> [apologies for multiple postings] www.dmu.ac.uk/ethicomp2015 <http://www.dmu.ac.uk/ethicomp2015> Conference title: 20 Years of ETHICOMP: A Celebration *De Montfort University, Leicester, UK*** Monday to Wednesday, 07.-09. September 2015 General Description In 1995 the first ETHICOMP conference was held in Leicester, England, organised by Terry Bynum and Simon Rogerson. Its purpose was to provide a forum to discuss ethical issues around computers. Twenty years later we are meeting again in Leicester to continue this conversation. The changes in information and communication technology (ICT) during these 20 years have been dramatic. While computers used to be bulky and easily identifiable machines, we now have small smart devices, the internet quickly developed and has changed significantly, and ICT now pervades all walks of life, from the way we work and communicate to study, undertake childcare and choose partners. As a consequence many of the concerns of 1995 have deepened and many new ones have arisen. During ETHICOMP 2015, we will review ethical and social issues raised by contemporary computing and look at ways of identifying and addressing them in the future. The conference aims to be practically relevant and bring together the various communities involved in the development, implementation, and use of computing and reflection on these in their various guises. The conference is based on the belief that the ETHICOMP community, together with other associations and groups, needs to work together to enable the benefits of computing to prevail, while rendering its downsides and ethical ambiguities visible and more subject to public debate than is the case today. To structure the discussion we invite submissions to the following tracks (check website for more detailed description of tracks): ·Researchers’ issues in Computer Ethics / Information Ethics studies (Gonçalo Jorge Morais Costa, Ma³gorzata Alicja P³otka) ·Social Impacts of Snowden's Revelations: Worldwide Cross-cultural Analyses (Kiyoshi Murata) ·Digital Do-It-Yourself (DiDIY) (Vincent C. Müller) ·Responsible Research & Innovation in Industry (Catherine Flick) ·ICT and Society: social accountability, professional ethics and the challenges of virtuality and the cloud (Diane Whitehouse) ·Teaching and professional ethics (Malgorzata Plotka, Gonçalo Jorge Morais Costa,) ·Robo-ethics (Kathleen Richardson) ·Open track (topics of relevance that do not fit any of the themes) (Mark Coeckelbergh) ·New ideas on bringing people together / novel formats (Andy Bissett) Abstracts covering one or several of these perspectives are called for from business, government, computer science, information systems, law, media, anthropology, andragogy, psychology, sociology and philosophy. Interdisciplinary papers and those from new researchers and practitioners are encouraged. A paper might take a conceptual, applied, practical or historical focus. Case studies and reports on lessons learned in practice are welcome. How to submit? As in previous ETHICOMP conferences, papers written in English and not published nor submitted elsewhere will be accepted on the basis of an extended abstract of between 800 and 1000 words after a careful review by Programme Committee members. Whilst more than one paper from an author or co-authors is welcomed, the final decision on which papers are accepted will probably lead to no more than two papers per presenting author being accepted. This will give more opportunity for as many people as possible to participate in ETHICOMP 2015. Abstracts and, where appropriate, full papers will be submitted and reviewed on the following website: https://easychair.org/conferences/?conf=ethicomp2015 Important Dates Call for abstracts01 Nov 14 Initial submission 23 Feb 15 Reviews due25 March Paper acceptance / rejection07 April Final full paper submissions 01 July Proceedings It is planned to publish the conference proceedings as a special issue of the ACM SIGCAS newsletter which will ensure inclusion in the ACM digital library (subject to confirmation). Selected papers will be invited to be submitted for a special issue of the Journal of International Technology and Information Management. Authors will be informed prior to final acceptance where and how final papers are to be uploaded. All authors whose abstracts are accepted are expected to submit a full paper to be included in the proceedings. If you have any questions, please do not hesitate to contact me. Kind regards, Bernd Bernd Carsten STAHL Professor of Critical Research in Technology Director, Centre for Computing and Social Responsibility School of Computer Science and Informatics De Montfort University The Gateway, Leicester, LE19BH, UK Tel: +44 116 207 8252 Web: www.dmu.ac.uk/berndstahl <www.dmu.ac.uk/berndstahl>

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Fwd: [AISWorld] Call For Papers - 1st International Workshop on Digital Business Innovation and the Future Enterprise Information Systems Engineering (DiFenSE 2015)
by Gustaf Neumann 22 Jan '15

22 Jan '15

-------- Weitergeleitete Nachricht -------- Betreff: [AISWorld] Call For Papers - 1st International Workshop on Digital Business Innovation and the Future Enterprise Information Systems Engineering (DiFenSE 2015) Datum: Wed, 21 Jan 2015 17:40:20 +0100 Von: Gianluigi Viscusi <gianluigi.viscusi(a)epfl.ch> An: aisworld(a)lists.aisnet.org DiFenSE 2015 â 1st International Workshop on Digital Business Innovation and the Future Enterprise Information Systems Engineering /Held in conjunction with the 27th International Conference on Advanced Information Systems Engineering CAiSEâ15. June 8-12, Stockholm, Sweden./ ------------------------------------------------------------------------ *Important dates* * Submission is open: 15th December 2014 * Submission deadline: 10th February 2015 * Notification of acceptance: 10th March 2015 * Camera-Ready copy due: 25th March 2015 * Workshop: 9th June 2015 ------------------------------------------------------------------------ Disruptive technological change has contributed in the last decade to accelerate the transition from a business-driven culture to a more âsocial-orientedâ one. Open innovation has become more influential and models of production and value creation are changing. The advent of social media, cloud computing, big data and the Internet of things outlines a new idea of socioeconomic organization, exemplified by the App Economy, already emerging as a collection of interlocking innovative ecosystems. âNew Forms of Enterprisesâ emerge, driven by constant business model transformation and innovation, acting as multi-sided platforms built on -as well as emerging from- digital innovations at the global, as well as local level, to produce shared value including that beyond monetisation. The scope of the 1st International Workshop on Digital Business Innovation and the Future Enterprise Information Systems Engineering (DiFenSE 2015) encompasses all aspects of digital business innovation and the role of information systems engineering and conceptual modelling for design thinking and the discovery and exploitation of digital opportunities. Thus, the workshop aims to promote and exchange ideas on the role and use of information systems engineering for future digital enterprises, digital business innovation and web entrepreneurship. The workshop is powered by the FP7 Future Enterprise Project Consortium. ------------------------------------------------------------------------ *Topics* We strongly encourage the publications reporting a synergy of innovative research and best practices in Digital Business Innovation, Future Internet-based Enterprise Systems, Sensing Enterprise, and Web Entrepreneurship. We encourage the original contributions exploring the challenges and solutions related to digital entrepreneurship and information systems engineering capabilities, as well as industrial case studies illustrating these challenges and solutions. The topics are among but not limited to: * Business Model Innovation Dynamics and tapping novel information systems engineering capabilities * Innovation strategies and IT implementations promoting new forms of enterprises in the Digital Economy * Digital Entrepreneurship through information systems engineering * Models and Engineering methods and Tools for the Sensing Enterprise * Agile models and open architectures for Future Internet-based enterprises * Internet Enterprise information systems architectures and interoperability * Dynamic Capabilities and information systems engineering * Value creation of Multi-sided platforms and IT visions of Internet-based Enterprise innovation * Enterprise Talent management and based on context-aware information systems engineering capabilities * Case studies of disruptive innovations by enterprises, SMEs, entrepreneurs in business sectors * Open scientific challenges in the technical foundations of Digital Business Innovation ------------------------------------------------------------------------ *Papers submission* We invite submissions in the form of Full papers and Short papers. * Full papers reporting the completed research, industrial case studies, empirical studies and surveys are limited to 12 pages including all text, figures, references and appendices. * Short papers reporting research-in-progress and problem statements are restricted to a maximum length of 6 pages (including all text, figures, references and appendices). * All papers should be submitted in PDF format. The results described must be unpublished and must not be under review elsewhere. Submission is done through easychair at the following page: https://easychair.org/conferences/?conf=difense2015 For updates we suggest to follow the DBI Community on: * Facebook <https://www.facebook.com/pages/Digital-Business-Innovation-Community/155816…> * Twitter <https://twitter.com/dbi_community> or * Linkedin Group <https://www.linkedin.com/groups/Digital-Business-Innovation-DBI-Community-3…> ------------------------------------------------------------------------ *Guidelines for authors* The accepted papers will be published in Springer LNBIP volume proceedings together with the other CAISE 2015 workshops. Thus, in order to ensure a timely and smooth publication process we would kindly ask you to follow the following instructions while submitting your paper. The submitted manuscripts should comply with the Springer LNBIP formatting rules. To prepare your initial submission, please follow the Author Instructions at http://www.springer.de/comp/lncs/authors.html Please make sure to use the full names of all contributing authors in the paper (first name(s) and surname). Please make sure that the figures are legible also when printed in black and white. Colored figures will only appear in the online version of the proceedings. ------------------------------------------------------------------------ *Organizers* *PC chair * * Fenareti Lampathaki (P.h.D.) National Technical University of Athens (NTUA) Iroon Polytechniou 9, 157 73 Athens Greece Email : flamp(a)epu.ntua.gr * Christopher Tucci (Ph.D.) EPFL CDM MTEI CSI ODY 1 04 (Odyssea) Station 5 CH-1015 Lausanne, Switzerland Email : christopher.tucci(a)epfl.ch * Gianluigi Viscusi (Ph.D.) EPFL CDM MTEI CSI ODY 1 16 (Odyssea) Station 5 CH-1015 Lausanne, Switzerland email: gianluigi.viscusi(a)epfl.ch *Program Committee* * Sabine Brunswicker, Purdue University, US * Jonathan Cave, University of Warwick, UK * Yannis Charalabidis University of Aegean, Greece * Fenareti Lampathaki, National Technical University of Athens (NTUA), Greece * John Psarras, National Technical University of Athens (NTUA), Greece * Sotiris Koussouris, National Technical University of Athens (NTUA), Greece * Christopher Tucci, EPFL, Switzerland * Gianluigi Viscusi, EPFL, Switzerland * /<<More members of the Program Committee to be announced soonâ¦>>/

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Fwd: [WI] 2nd Call for Papers: 2nd International Workshop on Process Modelling Support Systems (ProMoS 2015) at ECIS 2015
by Gustaf Neumann 22 Jan '15

22 Jan '15

-------- Weitergeleitete Nachricht -------- Betreff: [WI] 2nd Call for Papers: 2nd International Workshop on Process Modelling Support Systems (ProMoS 2015) at ECIS 2015 Datum: Thu, 22 Jan 2015 08:43:30 +0000 Von: Delfmann, Carsten Patrick <patrick.delfmann(a)ercis.uni-muenster.de> Antwort an: Delfmann, Carsten Patrick <patrick.delfmann(a)ercis.uni-muenster.de> An: wi(a)aifb.uni-karlsruhe.de <wi(a)aifb.uni-karlsruhe.de>, aisworld(a)lists.aisnet.org <aisworld(a)lists.aisnet.org> 2nd International Workshop on Process Modelling Support Systems (ProMoS 2015) at ECIS 2015 ------------------------------------------------------------------------------------------ 2nd Call for Papers --------------- Workshop Topic -------------- Conceptual modelling in Business Process Management (BPM) is one of the core research areas of Information Systems (IS). A variety of different strategies for modelling support exists such as syntax-based auto-completion features, knowledge-based recommender systems, correctness and compliance checking, abstraction and matching, semantic and domain patterns, or Artificial Intelligence-based planning approaches. These mechanisms increasingly gain attention in the BPM and conceptual modelling community. In particular, modelling support mechanisms are intended to ease the model construction, maintenance and analysis phases typically following a mixed-initiative approach, where the human and the support system work in close co-operation. Due to the great variety of techniques and use cases of modelling support systems, research is scattered amongst different sub-communities of the large BPM and conceptual modelling communities and a common ground for discussion and empirical evaluation of these approaches is not yet established. Therefore, we aim to bring together researchers working on modelling support techniques to discuss corresponding novel approaches and possibilities to evaluate their feasibility, effectiveness, efficiency, and usability. Therefore, contributors are invited to submit original research papers addressing relevant aspects of conceptual modelling support techniques in BPM. Topics of interest include, but are not limited to: - Conceptual modelling support methods and strategies - Conceptual modelling support features and algorithms - Querying, filtering and ranking of results - Visualization techniques and user interface - Architectures and implementation issues - Empirical studies evaluating modelling support systems Workshop Format --------------- The workshop is planned as a full day event comprising three technical sessions. Also, an additional demo session is planned where contributors will have the opportunity to present their process modelling support tools. A tool presentation is accompanied by a short paper. Submission Handling and Publication ----------------------------------- The workshop papers will be peer-reviewed. Accepted papers are planned be published as part of the CEUR Workshop Proceedings (CEUR-WS.org). Two kinds of contributions are sought: short position papers including tool presentation papers (not to exceed 6 pages) describing particular challenges or experiences relevant to the scope of the workshop, and full research papers (not to exceed 12 pages) describing novel solutions to the scope of the workshop. We plan to furthermore publish selected full papers from the workshop in a special issue of an international journal. In particular, we apply for a special issue of the Business Information Systems Engineering (BISE) Journal. Papers should comply with the LNI formatting guidelines and be submitted via the ProMoS EasyChair Account. Important Dates --------------- - Paper submission opens: 2014-12-01 - Paper submission deadline: 2015-02-01 - Reviews due: 2015-03-01 - Notification of acceptance: 2015-03-15 Workshop Organizers ------------------- - Patrick Delfmann, University of Münster - Michael Fellmann, University of Osnabrück - Agnes Koschmider, Karlsruhe Institut of Technology (KIT) The workshop is organized in co-operation with the working group Semantic Technologies in BPM of the special interest group of Enterprise Modelling and Information Systems Architectures (EMISA) of the German Association for Computer Science (GI). If you have any questions, please do not hesitate to contact one of the workshop organizers. Workshop Homepage ----------------- http://pmss.wi.uni-muenster.de/home -- Mailing-Liste: wi(a)lists.kit.edu Administrator: wi-request(a)lists.kit.edu Konfiguration: https://www.lists.kit.edu/wws/info/wi

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Fwd: [AISWorld] ICEC 2015 Call for Papers (Byungjoon Yoo)
by Gustaf Neumann 22 Jan '15

22 Jan '15

-------- Weitergeleitete Nachricht -------- Betreff: [AISWorld] ICEC 2015 Call for Papers (Byungjoon Yoo) Datum: Thu, 22 Jan 2015 13:04:46 +0900 Von: 안대환 <penris32(a)snu.ac.kr> An: aisworld(a)lists.aisnet.org Kopie (CC): 유병준 <byoo(a)snu.ac.kr> The 17th International Conference on Electronic Commerce (ICEC 2015) will be held in Seoul, Korea on August 3-5, 2015. The conference aims at providing researchers and practitioners in the information management, Computer Science, Industrial Engineering and related areas with an opportunity to present original ideas and share insightful opinions. The theme of ICEC 2015 is "Application of Big Data in E-Commerce." We welcome submissions of original research papers addressing issues concerning the theory, design, development, evaluation, and application of Electronic Commerce. We also encourage submissions of research-in-progress that are innovative and inspirational. Research articles particularly sought after are those driven by real-world business problems and validated with proper research methodologies. The relevant tracks and topics include, but are not limited to, the following: 01. Economics of E-Commerce 02. Mobile Payments 03. Social Commerce 04. Social Media and Digital Marketing 05. Data Mining 06. Big Data Analytics 07. Mobile Commerce 08.. Social Network Analysis 09. Human Computer Interactions 10. Cybersecurity 11. Health Informatics 12. General Track 13. Industrial Track Important dates: Deadline for paper submission: March 14, 2015 Paper Decision: April 25, 2015 Registration will start on: April, 2015 Deadline for camera-ready version of accepted papers and abstracts: May 16, 2015 Conference Honorary Co-Chairs - Jae Kyu Lee, KAIST, Korea - Robert J. Kauffman, Singapore Management University, Singapore Conference Co-Chairs - Zoonky Lee, Yonsei University, Korea - Karl R. Lang, City University of New York, U.S.A. Program Co-Chairs: - Byungjoon Yoo, Seoul National University, Korea - Ting Li, Erasmus University, Netherlands For more information. Please visit http://www.icec2015.org/ Best regards, ByungJoon Yoo On behalf of Zoonky Lee, Karl Lang, Co-chars of ICEC 2015

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Fwd: [AISWorld] IS-EUD 2015 Doctoral Consortium CALL FOR PARTICIPATION
by Gustaf Neumann 20 Jan '15

20 Jan '15

-------- Weitergeleitete Nachricht -------- Betreff: [AISWorld] IS-EUD 2015 Doctoral Consortium CALL FOR PARTICIPATION Datum: Tue, 20 Jan 2015 14:30:08 -0500 Von: Patrick Shih <patshih(a)gmail.com> An: aisworld(a)lists.aisnet.org [Apologies for cross-postings. Please send to interested colleagues and students] IS-EUD 2015 Doctoral Consortium CALL FOR PARTICIPATION http://dei.inf.uc3m.es/iseud2015/conference/doctoral-consortium/ ********************************************************************* 5th Symposium on End User Development: My world, my device, my program Madrid, 26-29 May 2015 ********************************************************************* IS-EUD Doctoral Consortium Call for submission - EXTENDED DEADLINE The IS-EUD Doctoral Consortium is intended to bring together PhD students working on theory and application of End-User Development. We particularly encourage students that are somewhere in the middle of their research to submit to this workshop.Â Submission Applications to the Doctoral Consortium (up to 4 pages) must be carefully formatted the Springer LNCS format (http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0). Authors may submit and update their submission through the EasyChair system (http://www.easychair.org/conferences/?conf=iseud2015) until the submission deadline. Important datesÂ Submission: February 2, 2015 (EXTENDED) Notification: February 28, 2015 Camera-ready: March 9, 2015 Doctoral Consortium chairs Clarisse de Souza. PUC-Rio, Brasil Panos Markopoulos. Eindhoven University of Technology, The Netherlands Simone Stumpf. City University London, UK ________________________________________________________ Dr. Patrick C. Shih College of Information Sciences and Technology The Pennsylvania State University ________________________________________________________

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Fwd: [AISWorld] Reminder: Call for Papers SCIS 2015 (dedline Feb 15th 2015)
by Gustaf Neumann 20 Jan '15

20 Jan '15

-------- Weitergeleitete Nachricht -------- Betreff: [AISWorld] Reminder: Call for Papers SCIS 2015 (dedline Feb 15th 2015) Datum: Tue, 20 Jan 2015 14:28:54 +0000 Von: Netta Iivari <Netta.Iivari(a)oulu.fi> An: aisworld(a)lists.aisnet.org <aisworld(a)lists.aisnet.org> *CALL FOR PAPERS* */The Sixth Scandinavian Conference on Information Systems (SCIS 2015)/* August 9-12, 2015 Oulu, Finland www.scis2015.org <http://www.scis2015.org> scis2015(a)oulu.fi *Important dates* *Submission deadline: *15.2.2015 *Notification to authors: *30.3.2015 *Final version due: *30.4.2015 *Registration due: *15.5.2015 University of Oulu welcomes you to/the Sixth Scandinavian Conference on Information Systems (SCIS 2015) /to be held in Oulu, Finland. The conference will be held in conjunction with the 38th Information Systems Research Seminar in Scandinavia (IRIS 2015). While many of the participants are from the Nordic countries, researchers from all parts of the world are warmly welcome to the conference. *Conference theme: System design for, with and by users* Information systems are in various forms embedded in everyday human practices. They have become intertwined and embedded in almost all parts of our everyday life, including leisure, thereby establishing new forms of participation and design. Hence, “users” cannot be considered as passive consumers anymore, but at least as content producers if not even as co-designers and innovators. This calls for reconsidering the traditional understandings of the roles of users and designers as well as for different development approaches that place emphasis on users’ empowerment, motivation and inclusion in designing, shaping, innovating and co-creating information systems in their everyday life. The SCIS conference theme challenges IS researchers to consider these new forms of participation, persuasion and design—/for, with and by users/—both in the sense of understanding the phenomenon better and in devising better support for it. We also call for reflective accounts on this decades old theme that has from the very beginning characterized Scandinavian IS design. *Paper submission* SCIS 2015 is open for full research papers within the conference theme as well as other areas of IS research. We welcome empirical as well as theoretical and methodological papers. Papers are not to exceed 6000 words/16 pages. Papers must be formatted according to the Springer LNBIP instructions and submitted through EasyChair: https://www.easychair.org/conferences/?conf=scis2015 Accepted papers will be published in the Springer series of Lecture Notes in Business Information Processing (LNBIP). *General Co-Chairs* Iivari Netta, University of Oulu, Finland Kuutti Kari, University of Oulu, Finland *Program Chair* Oinas-Kukkonen Harri, University of Oulu, Finland *Local Organizing Chair* Molin-Juustila Tonja, University of Oulu, Finland *Program Committee* Aaen Ivan, Aalborg University, Denmark Baghaei Nilufar, Unitec Institute of Technology, New Zealand Bratteteig Tone, University of Oslo, Norway Chatterjee Samir, Claremont Graduate University, US Cheung Christy, Hong Kong Baptist University, Hong Kong Chevalier Max, IRIT, France Eslambolchilar Parisa, Swansea University, UK Finken Sisse, Linnaeus University, Sweden Gholami Roya, Aston University, UK Gretzel Ulrike, University of Queensland, Australia Henfridsson Ola, University of Warwick, UK Iyengar Sriram, University of Texas Houston, US Krogstie John, Norwegian University of Science and Technology, Norway Lanamäki Arto, University of Oulu, Finland Mäntymäki Matti, University of Turku, Finland Mörtberg Christina, Linnaeus University, Sweden Oinas-Kukkonen Henry, University of Oulu, Finland Pekkola Samuli. Tampere University of Technology, Finland Päivärinta Tero, Luleå University of Technology, Sweden Riedl Rene, University of Linz, Austria Rossi Gustavo, University of La Plata, Argentina Rossi Matti, Aalto University, Finland Scupola Ada, Roskilde University, Denmark Simonsen Jesper, Roskilde University, Denmark Smolander Kari, Lappeenranta Institute of Technology, Finland Sørensen Carsten, London School of Economics, UK Sudzina Frantisek, Aalborg University, Denmark Suomi Reima, University of Turku, Finland Tørning Kristian, Danish School of Media and Journalism, Denmark Tuunainen Virpi, Aalto University, Finland Tuunanen Tuure, University of Jyväskylä, Finland van der Heijden Hans, University of London, UK Verner June, University of New South Wales, Australia Wiafe Isaac, Ghana Institute of Management and Public Administration, Ghana Wilson Vance, Worcester Polytechnic Institute, US Win Khin, University of Wollongong, Australia Yetim Fahri, FOM University of Applied Science, Germany Zhao Li, University of Oulu, Finland

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Fwd: [AISWorld] Call for contributions - Working Conferences and Workshops at CAiSE 2015
by Gustaf Neumann 19 Jan '15

19 Jan '15

-------- Weitergeleitete Nachricht -------- Betreff: [AISWorld] Call for contributions - Working Conferences and Workshops at CAiSE 2015 Datum: Mon, 19 Jan 2015 10:35:17 +0000 Von: Anne Persson <anne.persson(a)his.se> An: aisworld(a)lists.aisnet.org <aisworld(a)lists.aisnet.org> Dear Colleagues, The 27th International Conference on Advanced Information Systems Engineering (CAiSE 2015) will be held in Stockholm, Sweden in June 2015. The conference program features, in addition to regular paper presentations and other events, two working conferences and 13 thematic workshops covering av variety of interesting topics. The submission deadline for the main conference is closed but you are all invited to submit papers the working conferences and workshops. More information can be found here: http://caise2015.dsv.su.se/at-a-glance/working-conferences/ http://caise2015.dsv.su.se/at-a-glance/workshops More information about the CAiSE 2015 conference can be found here: http://caise2015.dsv.su.se Hope to see you in Stockholm, Anne Persson and Janis Stirna CAiSE 2015 Workshop Co-Chairs ______________________ Anne Persson Professor of Informatics University of Skövde PO Box 408, SE-541 28 Skövde, Sweden

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Fwd: [AISWorld] SPECIAL ISSUE ON Big Data and Business Analytics Adoption and Use: A Step toward Transforming Operations and Production Management
by Gustaf Neumann 19 Jan '15

19 Jan '15

-------- Weitergeleitete Nachricht -------- Betreff: [AISWorld] SPECIAL ISSUE ON Big Data and Business Analytics Adoption and Use: A Step toward Transforming Operations and Production Management Datum: Mon, 19 Jan 2015 14:14:08 +0000 Von: Samuel FOSSO WAMBA <Samuel.FOSSO.WAMBA(a)neoma-bs.fr> An: aisworld(a)lists.aisnet.org <aisworld(a)lists.aisnet.org> ********************* CALL FOR PAPERS ********************* SUBMISSION DUE DATE: February 15, 2015 Reviewer first reports: June 15, 2015 Revised paper submission: September 15, 2015 Reviewer second reports: December 10, 2015 Final manuscript submissions to publisher: March 15, 2016 SPECIAL ISSUE ON *Big Data and Business Analytics Adoption and Use: A Step toward Transforming Operations and Production Management?* ** International Journal of Operations & Production Management *Guest Editors: * Dr Samuel Fosso Wamba, Associate Professor, NEOMA Business School, France Dr Andrew Taylor, Professor, Bradford University School of Management, UK Dr Eric Ngai, Professor, The Hong Kong Polytechnic University, Hong Kong Dr Fred Riggins, Associate Professor, North Dakota State University, USA *Introduction:* Big data analytics is deï¬ned as â/a collection of data and technology that accesses, integrates, and reports all available data by filtering, correlating, and reporting insights not attainable with past data technologies/â (APICS 2012 <#_ENREF_1>). It is an emerging phenomenon which reflects the ever increasing significance of data in terms of its growing volumes, variety and velocity (the speed with which it is being created and processed) (Department for Business- Innovation and Skills 2013 <#_ENREF_3>). While data has always been a part of the Information and Communication Technology (ICT) agenda, it is the scale and scope of change which big data is bringing that has attracted so much attention. Like many new phenomena it is sometimes over-sold because of hype or misunderstanding, yet there are tangible case studies of the power of big data to generate value and competitive advantage, albeit such examples remain comparatively small in number to-date. Its applications have been strong in the financial services, insurance, retailing and healthcare sectors, while in manufacturing, companies such as Rolls Royce and Ford have been reported to derive success from big data in predicting engine failures before they occur and in managing supplier risk (Goodwin 2013 <#_ENREF_6>). For Operations Management, big data has the potential to enable more sophisticated data-driven decision making and new ways to organise, learn and innovate (Yiu 2012 <#_ENREF_13>; Kiron 2013 <#_ENREF_7>). Its impact may be manifest in strengthening customer relationships, managing operations risk, improving operational efficiency or by improving product or service delivery or whatever the key business drivers may be (Kiron 2013 <#_ENREF_7>). Operations in many organisations are experiencing much more voluminous and unstructured data environments because of real-time information from sensors and RFID tags which facilitate asset and business process monitoring (Davenport, Barth et al. 2012 <#_ENREF_2>), end-to-end supply chain visibility, improved manufacturing and industrial automation (Wilkins 2013 <#_ENREF_12>), manufacturing efficiency and effectiveness (Zelbst, Green et al. 2011 <#_ENREF_14>). Ford, for example, is reported to be scouring â/the metrics from the company's best processes across myriad manufacturing efforts and through detailed outputs from in-use automobiles--all to improve and help transform its business./â (Gardner 2013 <#_ENREF_4>). However, despite some reported successes, OM researchers need to retain a healthy scepticism until rigorous research has been done in operations contexts. That is why this new phenomenon should have the attention of OM researchers, and hence this call for papers. Given its high operational and strategic potential, notably in generating business value within various industries, big data has recently become the focus of a variety of scholars and practitioners. Some researchers have recently suggested that âbig dataâ is the ânext big thing in innovationâ (Gobble 2013, p.64 <#_ENREF_5>), âthe fourth paradigm of scienceâ (Strawn, (2012 <#_ENREF_10>)), or âthe next frontier for innovation, competition, and productivityâ (Manyika, Chui et al. 2011, p.1 <#_ENREF_9>). As a result, challenges related to big data have confronted businesses and organizations. In the operations and service contexts, big data also holds tremendous potential. In a recent survey study on third-party logistics services (3PL), (Langley 2014 <#_ENREF_8>) found that 97% of shippers and 93% of 3PLs âfeel strongly that improved, data-driven decision-making is essential to the future success of their supply chain activities and processesâ (p. 4), whereas approximately50% of each group disagrees that âbig data fuels these decisionsâ which shows how much potential for big data has still to be realized (p. 4). Big retailers are currently leveraging big data capabilities for improved customer experience, fraud reduction, and just-in-time recommendations (Tweney 2013 <#_ENREF_11>). In addition, big data technologies can be implemented in a range of applications including industrial automation <http://www.dpaonthenet.net/products/183/Control-Automation>tools, building management systems, production equipment, sales force information systems, and power plan conditions tools. For example, big data enabled-automation and manufacturing facilitates real-time detection and diagnosis of production issues, and thus reduces significantly downtime costs. Similarly, insights from big data analytics allows real-time process monitoring and measurement for improved quality management, logistics and order fulfilment cycles (Wilkins 2013 <#_ENREF_12>). In short, â/by observing causal factors for quality issues, process variability and energy efficiency through the manufacturing process, big data analysis becomes the basis for gaining a competitive advantage/â(Wilkins 2013 <#_ENREF_12>). Even if big data holds the capability of transforming competition and thus competitive advantage, many managers are still struggling to understand the concepts related to big data, consequently failing to capture business value from big data. In addition, very few empirical studies have been conducted on the real value from big data. *Objective:* The main objective of this special issue is to fill this knowledge gap. Specifically, this special issue aims to invite OM scholars and practitioners to look at the ways and means to co-create and capture business value from big data in terms of new business opportunities, improved performance, and competitive advantage. The results will in turn reveal the implications of big data on operations management practices and strategies. ** *Recommended Topics:* The topics to be discussed in this special issue include but are not limited to the following: Â·Assessment of the effect of big data on operations and production management systems Â·Assessment of the effect of big data on the decision-making processes in operations Â·Assessment of facilitators and inhibitors of big data adoption for logistics, order fulfilment, distribution and supply chain management Â·Big data-enabled business analytics at the plant location , organizational, and supply chain levels Â·In-depth & longitudinal case studies and pilot studies on the implementation of IT infrastructure to support big data initiatives for improved operations management, lean & agile operations, quality management in operations and supply chain management Â·Facilitation of innovative electronic business models and operations by using big data in various sectors (e.g., healthcare, retail industry, and manufacturing) Â·New theory development to explain the adoption and use of big data in operations at the organizational and inter-organizational levels Â·Empirical studies assessing the business value of big data in terms of quality management, new products and services design, improved internal and supply chain operations capabilities Â·Social media and big data in cloud for services, operations and production management transformation Â·Placement of data analytics and big data in cloud for services, operations and production management transformation *Submission Procedure* Prospective authorsare invited to submit papers for this special thematic issue on *â**Big Data Adoption and Use: A Step toward Transforming Operations and Production Managementâ*on or before February 15, 2015. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNALâS GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.emeraldinsight.com/products/journals/author_guidelines.htm?id=ij… TO SUBMISSION at: http://mc.manuscriptcentral.com/ijopm. ** ** *About *International Journal of Operations & Production Management Journal The International Journal of Operations & Production Management exists to provide a communication medium for all those working in the operations management field. This includes: â¢ Private and public sectors â¢ Manufacturing and service settings â¢ Academic institutions â¢ Consultancies. The content of the Journal focuses on topics which have a substantial management (as opposed to technical) content. A double-blind review process ensures the journal content's high quality, validity and relevance. *Editor-in-Chief:*Professor Steve Brown University of Exeter Business School, UK ** *All inquiries should be directed to the attention of:* Samuel Fosso Wamba Guest Editor E-mail:samuel.fosso.wamba@neoma-bs.fr <mailto:samuel.fosso.wamba@neoma-bs.fr> *All manuscript submissions to the special issue should be sent through the online submission system: * http://mc.manuscriptcentral.com/ijopm ** * * * * ** *Samuel Fosso Wamba, PhD., *is Associate Professor at NEOMA Business School, France. Prior, he was a Senior lecturer at the School of Information Systems & Technology (SISAT), University of Wollongong, Australia. He earned an MSc in mathematics, from the University of Sherbrooke in Canada, an MSc in e-commerce from HEC Montreal, Canada, and a Ph.D. in industrial engineering, from the Polytechnic School of Montreal, Canada. His current research focuses on business value of IT, business analytics, big data, inter-organisational system (e.g., RFID technology) adoption and use, e-government (e.g., open data), supply chain management, electronic commerce and mobile commerce. He has published papers in a number of international conferences and journals including /European Journal of Information Systems/, /Production Planning and Control/, /International Journal of Production Economics/, /Information Systems Frontiers/, /Business Process Management Journal/, /Proceedings of the IEEE, AMCIS, HICSS, ICIS, and PACIS/. He is organizing special issues on IT related topics for the Business Process Management Journal, Pacific Asia Journal of the Association for Information Systems, Journal of Medical Systems, Journal of Theoretical and Applied Electronic Commerce Research, Journal of Organizational and End User Computing, and Production Planning & Control. ** *Andrew Taylor, PhD* Andrew Taylor is Professor of Operations and Information Systems at Bradford School of Management, Andrew teaches World Class Operations, Resource Planning for Operations and Environmental Management & Quality Systems. He specialises in research relating to organisational performance improvement approaches such as Lean Systems, Performance Measurement and applications of new technologies such as Data Mining, Knowledge Management and 3D Printing. Professor Taylor has professional experience in aerospace, public utilities and government organisations, having worked in Short Brothers (now part of the Bombardier group), Northern Ireland Electricity and the Northern Ireland Training Authority. He has consulted widely. As a graduate of The Queenâs University of Belfast, Andrew holds a BSc in electronics and information systems, an MSc in industrial engineering and a PhD in manufacturing management. Previously Andrew Taylor was Professor of Information Management at Queenâs, Belfast where he worked for 12 years before coming to Bradford in 1996. His research work has been published in /Omega, International Journal of Operations and Production Management, International Journal of Production Economics, Expert Systems with Applications, European Journal of Information Systems, Communications of the ACM,/ /Information Systems Management/, /Production Planning and Control /and the /International Journal of Production Research/. ** *Eric W. T. Ngai, PhD* Prof. Eric Ngai is a Professor in the Department of Management and Marketing at The Hong Kong Polytechnic University. His current research interests are in the areas of E-commerce, Supply Chain Management, Decision Support Systems and RFID Technology and Applications. He has over 100 refereed international journal publications including /MIS Quarterly, Journal of Operations Management, Decision Support Systems, IEEE Transactions on Systems, Man and Cybernetics, Production & Operations Management,/ and others.He is an Associate Editor of /European Journal of Information Systems/ and /Information & Management/. He serves on editorial board of four international journals.Prof. Ngai has attained an /h/-index of 20, and received 1190 citations, /ISI Web of Science/. *Fred Riggins, PhD* Fred Riggins is Associate Professor in the College of Business at North Dakota State University. His research focuses on e-commerce, inter-organizational systems, RFID, and microfinance. He has published in leading journals including /Management Science/, /Journal of Management Information Systems/, /Journal of the Association for Information Systems, International Journal of RF Technologies/, /Electronic Commerce Research and Applications/, and /Communications of the ACM/. In a 2009 AIS publication, he ranked #9 on the list of top IS researchers from 2003-2007 based on number of publications and outlets. According to Google Scholar he has an /h/-index of 19 and over 2,500 citations. *References:* APICS (2012). APICS 2012 Big Data Insights and Innovations Executive Summary. Davenport, T. H., P. Barth, et al. (2012). "How Big Data Is Different." _MIT Sloan Management Review_ *54*(1): 43-46. Department for Business- Innovation and Skills (2013). Seizing the data opportunity: A strategy for UK data capability Gardner, D. (2013). "Ford scours for more big data to bolster quality, improve manufacturing, streamline processes." Retrieved 19th February 2014, from http://www.zdnet.com/ford-scours-for-more-big-data-to-bolster-quality-impro…. Gobble, M. M. (2013). "Big Data: The Next Big Thing in Innovation." _Research Technology Management_ *56*(1): 64-66. Goodwin, G. (2013). Takeaways from the MIT/Accenture Big Data in Manufacturing Conference. _MIT/Accenture Big Data in Manufacturing conference _Cambridge, USA. Kiron, D. (2013). "Organizational Alignment is Key to Big Data Success." _MIT Sloan Management Review_ *54*(3): 1-n/a. Langley, J. C. J. (2014). 2014 THIRD-PARTY LOGISTICS STUDY: The State of Logistics Outsourcing. Capgemini Consulting*: *56pp. Manyika, J., M. Chui, et al. (2011). Big data: The next frontier for innovation, competition, and productivity, McKinsey Global Institute. Strawn, G. O. (2012). "Scientific Research: How Many Paradigms?" _EDUCAUSE Review_ *47*(3): 26. Tweney, D. (2013). "Walmart scoops up Inkiru to bolster its âbig dataâ capabilities online." Retrieved 15 October, 2013, from http://venturebeat.com/2013/06/10/walmart-scoops-up-inkiru-to-bolster-its-b…. Wilkins, J. (2013). "Big data and its impact on manufacturing." Retrieved 17 February, 2014, from http://www.dpaonthenet.net/article/65238/Big-data-and-its-impact-on-manufac…. Yiu, C. (2012). The Big Data Opportunity: Making Government faster, smarter and more personal. _Policy Exchange_. London*: *36. Zelbst, P. J., K. W. J. R. Green, et al. (2011). "Radio Frequency Identification Techonology Utilization and Organizational Agility." _The Journal of Computer Information Systems_ *52*(1): 24-33.

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Fwd: [AISWorld] Decision Sciences Journal of Innovative Education Special Issue CFP
by Gustaf Neumann 19 Jan '15

19 Jan '15

-------- Weitergeleitete Nachricht -------- Betreff: [AISWorld] Decision Sciences Journal of Innovative Education Special Issue CFP Datum: Mon, 19 Jan 2015 11:38:48 -0600 Von: Sean B. Eom <sbeom(a)semo.edu> An: irma-l(a)irma-international.org, aisworld(a)lists.aisnet.org, aom <MG-ED-DV(a)aomlists.pace.edu>, LEARNING-TECHNOLOGY(a)LISTSERV.IEEE.ORG * * *Emacs! * *Call for Papers Special issue on Identifying and Managing Critical Success Factors of Online Education / /*Guest Editors Sean Eom, College of Business, Southeast Missouri State University Nicholas J. Ashill, College of Business, American University of Sharjah, United Arab Emirates J. B. (Ben) Arbaugh, University of Wisconsin Oshkosh */ Motivation and Background /* We are entering a golden age of e-learning. E-learning could be at a Tipping Point as Americans trust in the quality of e-learning grows, and the number of students who take at least one online course continues to increase. Now is the time to make e-learning more successful. The success of an e-learning system can be measured in terms of learning outcomes and learner satisfaction, two dependent constructs that have been widely accepted in the e-learning literature. Learning outcomes are measured by progress on relevant objectives set by the instructor including progress on gaining factual knowledge, learning fundamental principles, and learning to apply what is learned to improve problem solving. Learner satisfaction is measured by the degree of satisfaction with perceived outcomes of taking online courses, courses, and instructors. This special issue is dedicated to identifying and effectively managing critical success factors for e-learning that enable e-learning outcomes to equal if not surpass those of face-to-face instruction. Moreover, it seeks to draw on experience with e-learning systems to provide direction for future developments in this domain. Conceptual frameworks, qualitative research, and empirical studies in the following areas are encouraged · Review, critical analysis, and/or meta-analysis of past research to evaluate the current state of e-learning and to guide future directions for e-learning development · Conceptual frameworks for e-learning · Dimensions of e-learning systems § Human dimension · Students: Self-Motivation, Personality, Learning Styles · Instructors as Facilitators, Motivators, Moderators § Design dimension · Learning models (Objectivism, Constructivism, Collaborativism, Cognitive information processing, Socioculturalism) · Course content, structure, and infrastructure § Learning Management systems and Information technology *· Technology platforms and tools · Security considerations · Collaborative meetings and discussion tools · Student-created instructional materials *§ Learner control and self-regulated e learning · Problem based learning · Self-directed learning · Impact of interactions on e-learning outcomes · Instructor-student · Student-student · Student-content/learning management system · Learning outcomes and learner satisfaction § Development and validation of measurement instruments http://dsjie.org/Portals/0/Users/Vijay/Content/2016%20Special%20Issue.pdf */Review Process and Deadlines /* Manuscripts for the special issue should be submitted after the authors have carefully reviewed DSJIEs submission guidelines at http://dsjie.org/JournalMission/tabid/84/Default.aspx <http://dsjie.org/JournalMission/tabid/84/Default.aspx>. Authors submitting a manuscript should indicate that it is for the special issue on Identifying and Managing Critical Success Factors of Online Education. Deadlines for the special issue are as follows: June 15, 2015: Submission deadline for initial submission September 1, 2015: First-round decisions on all submitted manuscripts November 1, 2015: Submission deadline for invited revisions December 15, 2015: Final decisions For more information, please contact the editor (dsjie.editor(a)gmail.com <mailto:dsjie.editor@gmail.com>). Best regards, Sean Eom ********************************************* Sean Eom Professor of MIS Department of Accounting, MS 5815 Southeast Missouri State University Cape Girardeau, MO 63701 Tel. 573-651-2615 (Office) 573-271-8770 (Home) Fax 573-651-2992 e-mail: sbeom(a)semo.edu Http://cstl-hcb.semo.edu/eom/ <http://cstl-hcb.semo.edu/eom/>*********************************************

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