Research Enterprise Office Search Portal

All the employees in University Technology Petronas need to access information instantaneously in order to enhance their functionality and efficacy. Is it easy to collaborate and gather the right information at the right time? Is all the research within a company documented? Is it easily available to all employees? And what happens when an employee leaves the company? This project is an analysis of current practices and outcomes of the search portal and the nature of it as they are evolving in most of the organizations. The findings suggest that interest in search engines across a variety of industries is very high, the technological foundations are varied, and the major concerns revolve around achieving the correct amount and type of accurate research and garnering support for contributing to the search portal. Implications for practice and suggestions for future research are drawn from the study findings. This project focused on the search function. The research is on how to make this search portal useful to the University Technology Petronas (UTP) community that is the UTP staff and lecturers. These search portal solutions are ideal for operations and maintenance manuals that once were reserved for 3-inch thick binders sitting on the shelves of many treatment plants. Moving the manual standard procedures, troubleshooting, theory, alarms, and equipment descriptions to an electronic, web-based solution offers many benefits. For one, the information can be updated and kept current much more effectively because it can be changed in one place and instantly updated at all access points. By developing this search portal, the staff and lecturers will be able to get information fast and efficiently

Towards a Query Optimizer for Text-Centric Tasks

Text is ubiquitous and, not surprisingly, many important applications rely on textual data for a variety of tasks. As a notable example, information extraction applications derive structured relations from unstructured text; as another example, focused crawlers explore the web to locate pages about specific topics. Execution plans for text-centric tasks follow two general paradigms for processing a text database: either we can scan, or "crawl," the text database or, alternatively, we can exploit search engine indexes and retrieve the documents of interest via carefully crafted queries constructed in task-specific ways. The choice between crawl- and query-based execution plans can have a substantial impact on both execution time and output "completeness" (e.g., in terms of recall). Nevertheless, this choice is typically ad-hoc and based on heuristics or plain intuition. In this article, we present fundamental building blocks to make the choice of execution plans for text-centric tasks in an informed, cost-based way. Towards this goal, we show how to analyze query- and crawl-based plans in terms of both execution time and output completeness. We adapt results from random-graph theory and statistics to develop a rigorous cost model for the execution plans. Our cost model reflects the fact that the performance of the plans depends on fundamental task-specific properties of the underlying text databases. We identify these properties and present efficient techniques for estimating the associated parameters of the cost model. We also present two optimization approaches for text-centric tasks that rely on the cost-model parameters and select efficient execution plans. Overall, our optimization approaches help build efficient execution plans for a task, resulting in significant efficiency and output completeness benefits. We complement our results with a large-scale experimental evaluation for three important text-centric tasks and over multiple real-life data sets.Information Systems Working Papers Serie

Information Inflation: Can The Legal System Adapt?

Information is fundamental to the legal system. Accordingly, lawyers must understand that information, as a cultural and technological edifice, has profoundly and irrevocably changed. There has been a civilization- wide morph, or pulse, or one might say that information has evolved. This article discusses the new inflationary dynamic, which has caused written information to multiply by as much as ten thousand-fold recently. The resulting landscape has stressed the legal system and indeed, it is becoming prohibitively expensive for lawyers even to search through information. This is particularly true in litigation

Observable Behavior for Implicit User Modeling: A Framework and User Studies

This paper presents a framework for observable behavior that can be used as a basis for user modeling, and it reports the results of a pair of user studies that examine the joint utility of two specific behaviors. User models can be constructed by hand, or they can be learned automatically based on feedback provided by the user about the relevance of documents that they have examined. By observing user behavior, it is possible to obtain implicit feedback without requiring explicit relevance judgments. Four broad categories of potentially observable behavior are identified: examine, retain, reference, and annotate, and examples of specific behaviors within a category are further subdivided based on the natural scope of information objects being manipulated: segment, object, or class. Previous studies using Internet discussion groups (USENET news) have shown reading time to be a useful source of implicit feedback for predicting a user’s preferences. The experiments reported in this paper extend that work to academic and professional journal articles and abstracts, and explore the relationship between printing behavior and reading time. Two user studies were conducted in which undergraduate students examined articles or abstracts from the telecommunications or pharmaceutical literature. The results showed that reading time can be used to predict the user’s assessment of relevance, that the mean reading time for journal articles and technical abstracts is longer than has been reported for USENET news documents, and that printing events provide additional useful evidence about relevance beyond that which can be inferred from reading time. The paper concludes with a brief discussion of the implications of the reported results

Towards a Query Optimizer for Text-Centric Tasks

Text is ubiquitous and, not surprisingly, many important applications rely on textual data for a variety of tasks. As a notable example, information extraction applications derive structured relations from unstructured text; as another example, focused crawlers explore the Web to locate pages about specific topics. Execution plans for text-centric tasks follow two general paradigms for processing a text database: either we can scan, or “crawl,” the text database or, alternatively, we can exploit search engine indexes and retrieve the documents of interest via carefully crafted queries constructed in task-specific ways. The choice between crawl- and query-based execution plans can have a substantial impact on both execution time and output “completeness” (e.g., in terms of recall). Nevertheless, this choice is typically ad hoc and based on heuristics or plain intuition. In this article, we present fundamental building blocks to make the choice of execution plans for text-centric tasks in an informed, cost-based way. Towards this goal, we show how to analyze query- and crawl-based plans in terms of both execution time and output completeness. We adapt results from random-graph theory and statistics to develop a rigorous cost model for the execution plans. Our cost model reflects the fact that the performance of the plans depends on fundamental task-specific properties of the underlying text databases. We identify these properties and present efficient techniques for estimating the associated parameters of the cost model.We also present two optimization approaches for text-centric tasks that rely on the cost-model parameters and select efficient execution plans. Overall, our optimization approaches help build efficient execution plans for a task, resulting in significant efficiency and output completeness benefits. We complement our results with a large-scale experimental evaluation for three important text-centric tasks and over multiple real-life data sets.NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc

Personalized classification for keyword-based category profiles

Personalized classification refers to allowing users to define their own categories and automating the assignment of documents to these categories. In this paper, we examine the use of keywords to define personalized categories and propose the use of Support Vector Machine (SVM) to perform personalized classification. Two scenarios have been investigated. The first assumes that the personalized categories are defined in a flat category space. The second assumes that each personalized category is defined within a pre-defined general category that provides a more specific context for the personalized category. The training documents for personalized categories are obtained from a training document pool using a search engine and a set of keywords. Our experiments have delivered better classification results using the second scenario. We also conclude that the number of keywords used can be very small and increasing them does not always lead to better classification performance