Business Intelligence, Analytics and Cognitive: Case Studies and Applications (COGS) Minitrack

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The purpose of this minitrack is to invite case studies of both successful and unsuccessful, but informative, applications of business intelligence, data analytics, cognitive systems, & data, analytics & cognitive-enabled smart services across industries and societies. Business intelligence and data analytics have continued to make substantial inroads in the operational, managerial and strategic corporate decision-making processes. Recently, the emergence of cognitive computing systems that augment the creativity and productivity of people, and which are trained using artificial intelligence and machine learning algorithms to predict, infer and, up to some extent augment cognitive capabilities, has also extended the range of business intelligence and data analytics solutions on the market.

Multiple methods are encouraged. For example, instead of using an extensive statistical survey, case study, action research or design science may use qualitative methods to describe individual or organizational change. We are looking for reports that improve our understanding of how BI, Analytics, Cognitive technologies and Smart Services are currently used across industries and societies.

We encourage papers that report on lessons learned, on topics which include, but are not limited to, the following:

  • BI, Data Analytics & Cognitive: How to improve corporate data literacy
  • Data to Insight and Wisdom: Do universities make the grade?
  • The emergence of the Chief Analytics Officer and marketplace for specialists
  • The emergence of the Chief Digital Officer and marketplace for specialists
  • The diffusion stages of these information systems and competitive advantages
  • ROI of these information systems (BI, Analytics, Cognitive, Smart Services)
  • David vs. Goliath: SME and MNC using BI, Analytics, and Cognitive
  • “Big science” and “citizen science” applications of BI, Analytics, Cognitive and Smart Services
  • Opportunities and challenges of self-service, collaborative and mobile BI
  • Next generation of cognitive computing applications in business & education
  • Addressing grand challenges with intelligence, analytics, smart services, and cognitive assistants and mediators
  • Digital transformation with smart services and cognitive assistants

Minitrack Co-Chairs:

Sergey Belov (Primary Contact)
IBM East Europe/Asia

James C. Spohrer
IBM Almaden Research Center

Haluk Demirkan
University of Washington – Tacoma


Recent Submissions

Now showing 1 - 6 of 6
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    Towards Open Smart Services Platform
    ( 2017-01-04) Motahari Nezhad, Hamid Reza ; Shwartz, Larisa
    The landscape of services in the enterprise has changed significantly for both service providers and service clients over the last few years. In the IT services domain, the mega IT outsourcing service deals with a sole provider are diminishing fast. A typical service client is now consuming multiple IT services, from specialized providers, and services contracts has become smaller in size and duration. More importantly the line of business, not the IT, owns the decisions and the relationship for consuming services. This has also shifted the service consumption input from IT requirements into the business requirements. This new world is posing a new and unique set of opportunities and challenges for service providers in offering services, which include third party providers, to their clients, and for service clients to consume services from multiple providers. To facilitate offering and consuming such multi-vendor services, in this paper, we present a conceptual architecture for an open services platform which enables a given server provider (a service integrator) to offer services to its clients that are a mixture of its own and other services from third party providers. It also enables service clients to look for and choose services from multiple vendors in a seamless, integrated and consistent manner.
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    Overcoming Challenges to Effective Application of IT Research Results: A Framework for Encapsulating the Context and Findings of Research Studies
    ( 2017-01-04) Kettles, Degan ; Kulkarni, Uday ; Mazzola, Daniel ; St Louis, Robert
    Research studies frequently yield conflicting results. Resolving these conflicts is a grand challenge to effective application of research results. This paper presents a new framework for encapsulating the context and findings of research studies into a dimensional knowledge base which makes it easy to identify the conflicting results, and to explore the differences in context that might explain the conflicting findings. The framework is illustrated using the knowledge sharing domain. The Information Systems literature identifies over 100 variables associated with knowledge sharing, and the findings across different studies have frequently been contradictory. This paper shows how to capture the relevant contextual information, store the information in a dimensional document mart, and use the information to detect and reconcile seemingly inconsistent findings.
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    Modern Advanced Analytics Platforms and Predictive Models for Stock Price Forecasting: IBM Watson Analytics Case
    ( 2017-01-04) Faizullov, Ilias ; Yablonsky, Sergey
    The primary purpose of this paper was to provide an in-depth analysis of the ability of modern analytical platforms (using IBM Watson Analytics as an example) to generate predictive models for stock prices forecasting in comparison with traditional analytical econometric platforms and models. Series of stock predictive models based on the suggestions of IBM Watson Analytics have demonstrated results, which are superior to all other models. In terms of forecasting accuracy, they beat all models except for the Random Walk. The simulation has demonstrated high returns for most of the suggested models.
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    Identification of Human Factors in Aviation Incidents Using a Data Stream Approach
    ( 2017-01-04) Shi, Donghui ; Zurada, Jozef ; Guan, Jian
    This paper investigates the use of data streaming analytics to better predict the presence of human factors in aviation incidents with new incident reports. As new incidents data become available, the fresh information can help not only evaluate but also improve existing models. First, we use four algorithms in batch learning to establish a baseline for comparison purposes. These are NaiveBayes (NB), Cost Sensitive Classifier (CSC), Hoeffdingtree (VFDT), and OzabagADWIN (OBA). The traditional measure of the classification accuracy rate is used to test their performance. The results show that among the four, NB and CSC are the best classification algorithms. Then we test the classifiers in a data stream setting. The two performance measure methods Holdout and Interleaved Test-Then-Train or Prequential are used in this setting. The Kappa statistic charts of Prequential measure with a sliding window show that NB exhibits the best performance, and is better than the other algorithms. The two different measure methods, batch learning with 10-fold cross validation and data stream with Prequential measure, get one consistent result. CSC is a suitable for unbalanced data in batch learning, but it is not best in Kappa statistic for data stream. Valid incremental algorithms need to be developed for the data stream with unbalanced labels.
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    Fast Prototyping of the Internet of Things solutions with IBM Bluemix
    ( 2017-01-04) Popov, Aleksey ; Proletarsky, Andrey ; Belov, Sergey ; Sorokin, Alexander
    Fast prototyping for IoT projects has gained attraction in many industries. Today's IT market requires new faster techniques to get business advantages in different industries starting from the energy consumption and retail to the manufacturing, services, and agriculture. Combining sensors and actuators, embedded systems and networks with cloud computing platforms and cognitive services in one project is a very promising approach to address industry needs. Thus, system developers have to be familiar with many design technologies and best practices. At the same time, this approach requires a profound change in the way of interaction between major IoT market actors: suppliers and consumers of cloud platfоrm and services, teams of developers and universities. In this paper, we analyze how to build effectively interaction of major IoT market actors and discuss a platform for such collaboration. We present a collaborative framework for fast prototyping of IoT solutions with different stakeholders participating. The paper demonstrates the results of this approach in the case of interaction between vendor, university, and industry. We consider a number of technological and practical aspects of this collaborative framework using IBM Bluemix cloud platform and IoT templates. We tested this approach in IoT hackathon with a participation of a vendor, local business partners, and industry representatives. Projects developed during this hackathon will be used to illustrate results achieved by applying introduced concept for IoT solutions prototyping.
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