Since this is a rapidly evolving area, organizations continue to expend time and resources to enhance and develop new decision support applications and advance analytics to garner insights and knowledge. Research contributions in this space inform industry on how to handle the various organizational and technical opportunities and challenges when working with big data, knowledge management and analytics. From research on managerial concerns (such as strategy, governance, leadership), process-centric approaches and inter-organizational aspects of decision support to research on technical considerations when incorporating new data sources and new frameworks for big data, analytics and knowledge management, academic endeavors in this space provide insights on a dynamic and highly relevant field within information systems. This research track seeks research that promotes theoretical, design science, pedagogical, and behavioral research as well as emerging applications in innovative areas of analytics, big data, and knowledge management.
Research areas in big data, analytics and knowledge management (KM) include but are not limited to: data analytics & visualization; curation, management and infrastructure for (big) data; standards, semantics, privacy, security and legal issues in big data, analytics and KM; performance analysis, intelligence and scientific discovery in big data, analytics and KM and the like; analytics applications in smart cities, sustainability, smart grids and the like; business process management applications such as process discovery, conformance and mining using analytics and KM.
The quick and colossal rise of social media technologies such as twitter, wikis, blogs and other online social networks have created text corpora that allows enterprises to better develop and evaluate business decisions. While the main focus of today’s enterprise remains on mining structured and semi-structured enterprise data, this growth in the volume of text available online is providing new benefits in numerous domains such as marketing, finance, economics, and so forth. Researchers and scientists from different disciplines such as information systems, computer science, statistics, and linguistics are collaborating to define and assess new interdisciplinary theories and techniques pertaining to the exploitation of text corpora to solve operational and strategic business problems. To this end, the IS community is best positioned to lead research in this interdisciplinary subject as it touches on sciences relevant to the IS field such as information, data, design, and computer sciences. IS research can play a key role in exploring the opportunities and challenges that the available text corpora can present in today’s enterprise. We welcome empirical, theoretical, conceptual, and methodological research related to text mining and analytics and its relevance to today’s organizations.
Mini-Track 2: Business Analytics for Managing Organizational Performance
Benjamin Shao, Arizona State University, ben.shao@asu.edu
Robert D. St. Louis, Arizona State University, st.louis@asu.edu
The goal of business analytics (BA) is to summarize massive amounts of disparate corporate and customer data into succinct information that can help management better understand their business processes, make informed decisions, and measure and improve organizational performance. BA can provide managers with the ability to integrate enterprise-wide data into metrics that link specific objectives to the performance of different business units. In today’s hypercompetitive environment, accurate real-time BA metrics are even more critical for measuring and enhancing organizational performance. Many technologies contribute to BA solutions, including databases, data warehouses, data marts, analytic processing, social analytics, and data mining, among others. BA needs to acquire data from multiple platforms and provide ubiquitous access. This requirement to leverage so-called “big data” presents numerous managerial challenges. This mini-track aims to promote innovative research in the BA domains of organizational performance measurement and improvement.
Mini-Track 3: Spatial Business Intelligence, Location Analytics and Knowledge Management
James B. Pick, University of Redlands,
james_pick@redlands.edu
Daniel Farkas, Pace University,
dfarkas@pace.edu
Brian Hilton, Claremont Graduate University,
brian.hilton@cgu.edu
Avijit Sarkar, University of Redlands,
avijit_sarkar@redlands.edu
Hindupur Ramakrishna, University of Redlands,
hindupur_ramakrishna@redlands.edu
Namchul Shin, Pace University,
nshin@pace.eduThe mini-track provides a research forum on varied aspects of GIS for business intelligence, location-based analytics, knowledge management, and spatial data management. Topics include spatial big data, spatial decision support, spatial knowledge management, cloud-based GIS, spatial crowdsourcing, volunteered geographic information (VGI), spatial workforce development, managerial concerns, geo-design, privacy, security, and ethical aspects, mobile location-based applications, and emerging areas of GIS and location analytics. The mini-track encourages manuscript submission on theory, methodology, applications, behavioral studies, case studies, and emerging areas. Spatial technologies have been undergoing a major transformation based on new and emerging geospatial technologies including space-time, 3-D modeling, LIDAR, unmanned spatial data collection, augmented reality glasses, and virtual reality of place. The relevance to research is to build up greater knowledge of the geo-spatial aspects of decision-making and management, and to develop theory and applications, sometimes building on well-known concepts in the Decision Support Systems, Analytics and MIS fields. GIS and spatial technologies are growing rapidly and becoming essential in business and government. As the organizer of this mini-track, SIGGIS encourages and invites papers on the aforementioned topics addressing important spatial questions in MIS, business, and society. More detailed information is available at
http://siggis.wikispaces.com.
Mini-Track 4: Sports Analytics
Effective management of sports requires deep understanding of various sports related operations as well as prevention of player injuries. While sports related operations such as game marketing, fan engagement, ticket sale, team ranking, talent scouting, etc. are important for revenue purposes, it is critical that issues such as player performance & fatigue, injury risk prevention, concussions, athletic training and sports rehab, etc. that are central to sports science and player wellness are not ignored. More recently, integration of wearable technologies in sports is enabling generation of new types of data sets that are providing for a fertile playground to apply analytics to reap immediate benefits. Data science could play a tremendous role in the early identification or proper management of such injuries leading to improved long-term outcomes in areas such as quality of life, disability, concussion, musculoskeletal injuries, etc. This mini track invites original and high quality submissions from all aspects of sports that apply analytics, including injuries and concussions.
Mini-Track 5: Big Data Analytics for New Innovation Ecosystem
Applications of big data are leading towards new innovation ecosystem in the industry, academic research and government. The mini track focuses on the applications of big data paradigm and methodologies that lead to new knowledge discovery for the broader fields of sciences and not just limiting to Information technologies. The big data applications in each of these areas may be distinguished from traditional analytics studies using the reference of the traditional definition of ‘Big Data’, which is implies the presence of 4V’s as data characteristic- velocity, volume, veracity and variety. In addition to the application of big data to solve problems of national and regional priorities, suggested topics include, cognitive computing, real time analytics, Image recognition, advanced manufacturing, security, habitat planning and environment, healthcare, costal hazards and climate, bioinformatics and genomics, precision medicine, urban science, Food, water and energy, digital Agriculture (e.g. precision farming, sustainability), education, etc.
Mini-Track 6: Social Network Analytics in Big Data Environment
Social Network Analytics is the practice of measuring, analyzing and interpreting interactions and associations between people, topics, information, and ideas to uncover hidden patterns and correlations to assist in making more informed decisions. Emerging research in social network analytics is focusing on innovative methods and approaches for gathering disparate data from a variety of online social media fora, websites, and blogs and applying it to examine a range of questions pertaining to organizational, educational, social as well as political issues (e.g., role of social media analytics in Barack Obama’s re-election, and also predicting the right kind of flu virus using social media analytics). In this mini-track, we solicit high-quality original research papers, both theoretical and empirical, that address a variety of social network analytics issues and its applications in different contexts such as business, healthcare, education, politics, security and privacy, visual, and predictive analytics.
Mini-Track 7: Analytics and big data to support supply chain, operations, and logistics management
Analytics and “big data” describe a broad array of data-driven business practices that are reshaping the way by which firms compete in the marketplace. In global multi-channel multi-modal complex supply chains systems, decision support based on analytics is critical for organizations to plan and implement superior, well-coordinated, flexible, and responsive supply chain to better meet customer’s expectations and organizational goals. Data intensive decision support systems that incorporate analytics and data visualization are key to more efficient end-to-end management of in-sync supply chains and in house or third party logistics (3PL). Spatial optimization of supply chains is key to respond to price differentials across geographical locations. Knowledge management in intelligent transport systems (ITS) is critical to next generation of well-coordinated transportation and supply chain network systems for both people and goods. Time sensitive supply chains can benefit the most from new innovative developments in data reporting, verification, authentication and collaboration. Research is needed to build theory and inform practice regarding means through which firms adopt and use analytics and big data to support supply chain, operations, and logistics management applications. This minitrack solicits research papers covering a wide range of topics related to data analytics in the supply chain.
Click here to return to the track list page.
