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.

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Business intelligence and analytics: from big data to big impact

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

Recent technological advancements have led to a deluge of data from distinctive domains (e.g., health care and scientific sensors, user-generated data, Internet and financial companies, and supply chain systems) over the past two decades. The term big data was coined to capture the meaning of this emerging trend. In addition to its sheer volume, big data also exhibits other unique characteristics as compared with traditional data. For instance, big data is commonly unstructured and require more real-time analysis. This development calls for new system architectures for data acquisition, transmission, storage, and large-scale data processing mechanisms. In this paper, we present a literature survey and system tutorial for big data analytics platforms, aiming to provide an overall picture for nonexpert readers and instill a do-it-yourself spirit for advanced audiences to customize their own big-data solutions. First, we present the definition of big data and discuss big data challenges. Next, we present a systematic framework to decompose big data systems into four sequential modules, namely data generation, data acquisition, data storage, and data analytics. These four modules form a big data value chain. Following that, we present a detailed survey of numerous approaches and mechanisms from research and industry communities. In addition, we present the prevalent Hadoop framework for addressing big data challenges. Finally, we outline several evaluation benchmarks and potential research directions for big data systems.

Toward Scalable Systems for Big Data Analytics: A Technology Tutorial

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The TREC-8 Question Answering Track Report

The various methods and devices described herein relate to devices which, in at least certain embodiments, may include one or more sensors for providing data relating to user activity and at least one processor for causing the device to respond based on the user activity which was determined, at least in part, through the sensors. The response by the device may include a change of state of the device, and the response may be automatically performed after the user activity is determined.

Automated Response to and Sensing of User Activity in Portable Devices

The present invention provides an interface and associated object model that exposes a comprehensive set of natural language processing features to an application developer. In one embodiment, the features include lexicon management services and proofing services.

Natural language processing interface

In a method of processing an input query, an input query is received and a related query is selected from a query log. Next, the selected query is provided to a query processing system in place of the original input query. The present invention is also directed to a query modification system that is configured to perform the above-described method.

Query expansion using query logs

It addresses all its goals well and in general is very much forward-looking. Apart from an interesting paper on the commercial deployment of question answering applications, there is little historical review of the development of the field, and the extent to which it has been driven by the TREC-QA track evaluations, which have played a significant role in defining the field to date. The editor’s introduction and several of the papers make clear the extent to which question answering (QA) is a proper subset of artificial intelligence (AI). The editor summarises the results of a panel session at the symposium to establish a roadmap for QA research, which raises several significant issues as requirements, including entire AI research areas such as user modelling and inferencing. The discussion of the roadmap is very brief and would perhaps have deserved its own chapter in the book, to better situate the symposium papers. The introduction also contains a useful table of the areas addressed by each paper in the collection. However the table does act to highlight the fact that none of the papers address multilingual issues, which are a significant area for future research. The editor’s introductions to each section are extremely thorough and well written, providing very useful abstracts of all the papers’ main results and contributions. Several of the papers have rather tangential relations to QA, but their relevance and contribution are made clear by the editor’s abstracts. Section 1: Foundation and History situates the book well, giving a broad view of the long term goals for QA as an AI research area, together with a review of practical progress so far and the current status. The first chapter by Nyberg, Burger, Mardis and Ferrucci follows on from the discussion of the roadmap to give a high-level view of the criteria required for QA in general. It makes clear that fully general QA is a very ambitious goal, requiring solutions to various significant AI problems, but it does highlight real systems which are addressing some of the criteria. The next chapter by Ulicny gives a thorough

New Directions in Question Answering

Methods of identifying named entities in natural language text using machine or computer compiler tools are provided. A lexical analyzer generator such as Flex or Lex or an equivalent tool can be used to generate a recognizer for named entities, such as digits, date expressions, and email or web addresses. Alternatively, a parser generator, such as Yacc or Bison or an equivalent tool can be used to generate a recognizer for other named entities, such as person and company names. Further, a lexical analyzer generated by Flex, Lex, or its equivalent is used in combination with a parser generated by Yacc, Bison, or its equivalent to identify named entities. Multiple lexical analyzers and/or parsers identify one or more classes of named entities, such as email addresses or person names. In many embodiments, recognized named entities can be used to construct at least one index of web pages or documents including named entities that can be accessed by a natural language processing application.

Named entity recognition using compiler methods

A method of, and system for, generating a sentence from a semantic representation maps the semantic representation to an unordered set of syntactic nodes. Simplified generation grammar rules and statistical goodness measure values from a corresponding analysis grammar are then used to create a tree structure to order the syntactic nodes. The sentence is then generated from the tree structure. The generation grammar is a simplified (context free) version of a corresponding full (context sensitive) analysis grammar. In the generation grammar, conditions on each rule are ignored except those directly related to the semantic representation. The statistical goodness measure values, which are calculated through an analysis training phase in which a corpus of example sentences is processed using the full analysis grammar, are used to guide the generation choice to prefer substructures most commonly found in a particular syntactic/semantic context during analysis.

Kevin Humphreys

Papers

Natural language processing interface

Query expansion using query logs

New Directions in Question Answering

Named entity recognition using compiler methods

Statistically driven sentence realizing method and apparatus