Roger H. L. Chiang

Business intelligence and analytics: from big data to big impact

Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework.

Reverse engineering of relational databases: extraction of an EER model from a relational database

Abstract A methodology for extracting an extended Entity-Relationship (EER) model from a relational database is presented. Through a combination of data schema and data instance analysis, an EER model is derived which is semantically richer and more comprehensible for maintenance and design purposes than the original database. Classification schemes for relations and attributes necessary for the EER model extraction are derived and justified. These have been demonstrated to be implementable in a knowledge-based system; a working prototype system which does so is briefly discussed. In addition, cases in which human input is required are also clearly identified. This research also illustrates that the database reverse engineering process can be implemented at a high level of automation.

Quality-aware collaborative question answering: methods and evaluation

Community Question Answering (QA) portals contain questions and answers contributed by hundreds of millions of users. These databases of questions and answers are of great value if they can be used directly to answer questions from any user. In this research, we address this collaborative QA task by drawing knowledge from the crowds in community QA portals such as Yahoo! Answers. Despite their popularity, it is well known that answers in community QA portals have unequal quality. We therefore propose a quality-aware framework to design methods that select answers from a community QA portal considering answer quality in addition to answer relevance. Besides using answer features for determining answer quality, we introduce several other quality-aware QA methods using answer quality derived from the expertise of answerers. Such expertise can be question independent or question dependent. We evaluate our proposed methods using a database of 95K questions and 537K answers obtained from Yahoo! Answers. Our experiments have shown that answer quality can improve QA performance significantly. Furthermore, question dependent expertise based methods are shown to outperform methods using answer features only. It is also found that there are also good answers not among the best answers identified by Yahoo! Answers users.

Business Intelligence and Analytics Education, and Program Development: A Unique Opportunity for the Information Systems Discipline

“Big Data,” huge volumes of data in both structured and unstructured forms generated by the Internet, social media, and computerized transactions, is straining our technical capacity to manage it. More importantly, the new challenge is to develop the capability to understand and interpret the burgeoning volume of data to take advantage of the opportunities it provides in many human endeavors, ranging from science to business. Data Science, and in business schools, Business Intelligence and Analytics (BI&A) are emerging disciplines that seek to address the demands of this new era. Big Data and BI&A present unique challenges and opportunities not only for the research community, but also for Information Systems (IS) programs at business schools. In this essay, we provide a brief overview of BI&A, speculate on the role of BI&A education in business schools, present the challenges facing IS departments, and discuss the role of IS curricula and program development, in delivering BI&A education. We contend that a new vision for the IS discipline should address these challenges.

Database design with common sense business reasoning and learning

Automated database design systems embody knowledge about the database design process. However, their lack of knowledge about the domains for which databases are being developed significantly limits their usefulness. A methodology for acquiring and using general world knowledge about business for database design has been developed and implemented in a system called the Common Sense Business Reasoner, which acquires facts about application domains and organizes them into a a hierarchical, context-dependent knowledge base. This knowledge is used to make intelligent suggestions to a user about the entities, attributes, and relationships to include in a database design. A distance function approach is employed for integrating specific facts, obtained from individual design sessions, into the knowledge base (learning) and for applying the knowledge to subsequent design problems (reasoning).

Instance-based attribute identification in database integration

Abstract.Most research on attribute identification in database integration has focused on integrating attributes using schema and summary information derived from the attribute values. No research has attempted to fully explore the use of attribute values to perform attribute identification. We propose an attribute identification method that employs schema and summary instance information as well as properties of attributes derived from their instances. Unlike other attribute identification methods that match only single attributes, our method matches attribute groups for integration. Because our attribute identification method fully explores data instances, it can identify corresponding attributes to be integrated even when schema information is misleading. Three experiments were performed to validate our attribute identification method. In the first experiment, the heuristic rules derived for attribute classification were evaluated on 119 attributes from nine public domain data sets. The second was a controlled experiment validating the robustness of the proposed attribute identification method by introducing erroneous data. The third experiment evaluated the proposed attribute identification method on five data sets extracted from online music stores. The results demonstrated the viability of the proposed method.

A smart web query method for semantic retrieval of web data

Abstract The efficient query and extraction of web data is often difficult, because web data does not conform to any data organization standard. In addition, the development of web search technology is still at a relatively early stage. Search engines provide only primitive data query capabilities, and require a detailed syntactic specification to retrieve relevant data. Furthermore, web data exists in a myriad of formats including PDF documents, images, and sound clips that are difficult to be queried. This research proposes a smart web query (SWQ) method for the semantic retrieval of web data. The SWQ method uses domain semantics represented as context ontologies to specify and formulate appropriate web queries to search. This method also relies on semantic search filters to identify and rank relevant web pages semi-automatically. Unlike traditional ontologies that are structured in a hierarchy, terms and their relationships that pertain to a particular domain are organized with a flexible structure by the context ontologies. An SWQ engine is being developed to test the proposed method. Financial trading (e.g. stocks, bonds, unit trusts) is adapted as an example domain (i.e., context) to test and validate the SWQ method and engine.

A framework for the design and evaluation of reverse engineering methods for relational databases

Abstract It is often difficult to obtain a good conceptual understanding of a legacy database, especially when there is a lack of documentation. Database reverse engineering attempts to provide solutions for this problem. It is the part of system maintenance work that produces a sufficient understanding of a legacy database and its application domain to allow appropriate changes to be made. However, research on database reverse engineering has largely ignored design and evaluation issues of their methods (i.e., foundations and processes). This research proposes a framework for the design and evaluation of reverse engineering methods of relational databases. This framework consists of eight criteria: 1) the situation chosen as the basis for reverse engineering, 2) the conceptual model chosen to represent the reverse engineering results, 3) the prerequisites of the database to be reverse engineered, 4) the thoroughness of domain semantics acquisition, 5) rules and heuristics employed by the reverse engineering process, 6) performance efficiency of the reverse engineering process, 7) completeness and robustness and 8) validation. These criteria are important to be considered in designing reverse engineering methods, so that they can perform reverse engineering for a broad range of legacy databases at a high level of automation and provide a conceptual schema that is semantically rich and correct.

Accommodating Individual Preferences in the Categorization of Documents: A Personalized Clustering Approach

As electronic commerce and knowledge economy environments proliferate, both individuals and organizations increasingly generate and consume large amounts of online information, typically available as textual documents. To manage this ever-increasing volume of documents, individuals and organizations frequently organize their documents into categories that facilitate document management and subsequent access and browsing. Document clustering is an intentional act that should reflect individual preferences with regard to the semantic coherency and relevant categorization of documents. Hence, effective document clustering must consider individual preferences and needs to support personalization in document categorization. In this paper, we present an automatic document-clustering approach that incorporates an individuals partial clustering as preferential information. Combining two document representation methods, feature refinement and feature weighting, with two clustering methods, precluster-based hierarchical agglomerative clustering (HAC) and atomic-based HAC, we establish four personalized document-clustering techniques. Using a traditional content-based document-clustering technique as a performance benchmark, we find that the proposed personalized document-clustering techniques improve clustering effectiveness, as measured by cluster precision and cluster recall.

Enterprise integration with advanced information technologies: ERP and data warehousing

In today’s dynamic and changing environment, companies have a strong need to create or sustain their competitive advantages. In order to be competitive, companies need to be responsive and closer to the customers, and deliver value‐added products and services as quickly as possible. Companies also need to be able to support organizational information needs faster and better than their competitors. These goals can be realized by applying two emerging information technologies: enterprise resource planning (ERP) supporting business process integration; and data warehousing supporting data integration. Companies with the further integration of ERP and data warehousing will have great advantages in the competitive environment. Two cases have been studied and presented to illustrate its values.