Vasant Dhar

Supporting systems development by capturing deliberations during requirements engineering

Support for various stakeholders involved in software projects (designers, maintenance personnel, project managers and executives, end users) can be provided by capturing the history about design decisions in the early stages of the systems development life cycle in a structured manner. Much of this knowledge, which is called the process knowledge, involving the deliberation on alternative requirements and design decisions, is lost in the course of designing and changing such systems. Using an empirical study of problem-solving behavior of individual and groups of information systems professionals, a conceptual model called REMAP (representation and maintenance of process knowledge) that relates process knowledge to the objects that are created during the requirements engineering process has been developed. A prototype environment that provides assistance to the various stakeholders involved in the design and management of large systems has been implemented. >

Does Chatter Matter? the Impact of User-Generated Content on Music Sales

The Internet has enabled a new era of user-generated content, threatening the hegemony of traditional content generators as the primary sources of “legitimate” information. In this study, we examine the usefulness of such content, consisting of data from blogs and social networking sites, in predicting sales in the music industry. We track changes in online chatter for a sample of 108 albums for four weeks before and after their release dates. We identify the significance of variables on the observation date in predicting future album unit sales one, two, and three weeks ahead. Our findings are that future sales are positively correlated with (a) the volume of blog posts about an album, and (b) traditional factors such as whether the album is released by a major label and reviews from mainstream sources like Rolling Stone. More generally, the study provides some preliminary answers for marketing managers interested in assessing the relative importance of the burgeoning number of “Web 2.0” information metrics that are becoming available on the Internet. The study also provides a framework for thinking about when user-generated content influences decision making.

Data science and prediction

Big data promises automated actionable knowledge creation and predictive models for use by both humans and computers.

Editorial-Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research

We address key questions related to the explosion of interest in the emerging fields of big data, analytics, and data science. We discuss the novelty of the fields and whether the underlying questions are fundamentally different, the strengths that the information systems IS community brings to this discourse, interesting research questions for IS scholars, the role of predictive and explanatory modeling, and how research in this emerging area should be evaluated for contribution and significance.

Abstract-driven pattern discovery in databases

The problem of discovering interesting patterns in large volumes of data is studied. Patterns can be expressed not only in terms of the database schema but also in user-defined terms, such as relational views and classification hierarchies. The user-defined terminology is stored in a data dictionary that maps it into the language of the database schema. A pattern is defined as a deductive rule expressed in user-defined terms that has a degree of uncertainty associated with it. Methods are presented for discovering interesting patterns based on abstracts which are summaries of the data expressed in the language of the user. >

Cognitive process as a basis for intelligent retrieval systems design

Abstract Two studies were conducted to investigate the cognitive processes involved in online document-based information retrieval. These studies led to the development of five computational models of online document retrieval. These models were then incorporated into the design of an “intelligent” document-based retrieval system. Following a discussion of this system, we discuss the broader implications of our research for the design of information retrieval systems.

Integer programming vs. expert systems: an experimental comparison

Expert system and integer programming formulations of an NP-complete constraint satisfaction problem are contrasted in terms of performance, ability to encode complex preferences, control of reasoning, and supporting incremental modification of solutions in response to changing input data.

Discovering Interesting Patterns for Investment Decision Making with GLOWER x-A Genetic Learner Overlaid with Entropy Reduction

Prediction in financial domains is notoriously difficult for a number of reasons. First, theories tend to be weak or non-existent, which makes problem formulation open ended by forcing us to consider a large number of independent variables and thereby increasing the dimensionality of the search space. Second, the weak relationships among variables tend to be nonlinear, and may hold only in limited areas of the search space. Third, in financial practice, where analysts conduct extensive manual analysis of historically well performing indicators, a key is to find the hidden interactions among variables that perform well in combination. Unfortunately, these are exactly the patterns that the greedy search biases incorporated by many standard rule learning algorithms will miss. In this paper, we describe and evaluate several variations of a new genetic learning algorithm (GLOWER) on a variety of data sets. The design of GLOWER has been motivated by financial prediction problems, but incorporates successful ideas from tree induction and rule learning. We examine the performance of several GLOWER variants on two UCI data sets as well as on a standard financial prediction problem (S&P500 stock returns), using the results to identify one of the better variants for further comparisons. We introduce a new (to KDD) financial prediction problem (predicting positive and negative earnings surprises), and experiment with GLOWER, contrasting it with tree- and rule-induction approaches. Our results are encouraging, showing that GLOWER has the ability to uncover effective patterns for difficult problems that have weak structure and significant nonlinearities.

Dependence directed reasoning and learning in systems maintenance support

The maintenance of large information systems involves continuous modifications in response to evolving business conditions or changing user requirements. Based on evidence from a case study, it is shown that the system maintenance activity would benefit greatly if the process knowledge reflecting the teleology of a design could be captured and used in order to reason about he consequences of changing conditions or requirements, A formalism called REMAP (representation and maintenance of process knowledge) that accumulates design process knowledge to manage systems evolution is described. To accomplish this, REMAP acquires and maintains dependencies among the design decisions made during a prototyping process, and is able to learn general domain-specific design rules on which such dependencies are based. This knowledge cannot only be applied to prototype refinement and systems maintenance, but can also support the reuse of existing design or software fragments to construct similar ones using analogical reasoning techniques. >

User misconceptions of information retrieval systems

In this paper, we report results of an investigation where thirty subjects were observedperforming subject-based search in an online catalog system. The observationshave revealed a range of misconceptions users have when performing subject-basedsearch. We have developed a taxonomy that characterizes these misconceptions andhypotheses about the causes of the misconceptions. Directions for improving searchperformance are also suggested.