23,575 research outputs found
Using Search Engine Technology to Improve Library Catalogs
This chapter outlines how search engine technology can be used in online public access library
catalogs (OPACs) to help improve usersâ experiences, to identify usersâ intentions, and to indicate
how it can be applied in the library context, along with how sophisticated ranking criteria can be
applied to the online library catalog. A review of the literature and current OPAC developments
form the basis of recommendations on how to improve OPACs. Findings were that the major
shortcomings of current OPACs are that they are not sufficiently user-centered and that their results
presentations lack sophistication. Further, these shortcomings are not addressed in current 2.0
developments. It is argued that OPAC development should be made search-centered before
additional features are applied. While the recommendations on ranking functionality and the use of
user intentions are only conceptual and not yet applied to a library catalogue, practitioners will find
recommendations for developing better OPACs in this chapter. In short, readers will find a
systematic view on how the search enginesâ strengths can be applied to improving librariesâ online
catalogs
KNOWLEDGE DISCOVERY FROM DATABASES: THE NYU PROJECT
More and more application domains, from financial market analysis to weather
prediction, from monitoring supermarket purchases to monitoring satellite images, are
becomingly increasingly data-intensive. The result is massive databases that are growing
at a rapid rate - it has been estimated that the worldĂąs electronic data almost
doubles every year. With this rate of data explosion, there is a pressing need for computers
to play an increasing role in analyzing these huge data repositories which are
impossible to penetrate manually. The challenge is to ferret out the regularities in the
data that will prove to be interesting to the user.
A group in the Information Systems department at the NYU Business School has
been working in this area for a number of years. The focus of our project is now on the
discovery of patterns from time series data. In this paper we give an overview of the
kinds of databases we are "miningĂą and the kinds of temporal patterns and rules which
we are attempting to discover. In the first phase of this research, we have developed a
taxonomy of patterns as a way to organize our research agenda. We wish to share the
taxonomy with the research community in the "knowledge discovery in databases" area
since we have found it useful in classifying the universe of regularities or patterns into
distinct types, that is, patterns which differ in terms of their structure and the amount
6f search effort required to find them. Although the primary focus of our project is
on time series data, and the examples we will present are chosen from this arena, the
taxonomy is general enough to apply to any type of data.Information Systems Working Papers Serie
A survey of temporal knowledge discovery paradigms and methods
With the increase in the size of data sets, data mining has recently become an important research topic and is receiving substantial interest from both academia and industry. At the same time, interest in temporal databases has been increasing and a growing number of both prototype and implemented systems are using an enhanced temporal understanding to explain aspects of behavior associated with the implicit time-varying nature of the universe. This paper investigates the confluence of these two areas, surveys the work to date, and explores the issues involved and the outstanding problems in temporal data mining
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Constructing secure service compositions with patterns
In service based applications, it is often necessary to construct compositions of services in order to provide required functionality in cases where this is not possible through the use of a single service. Whilst creating service compositions, it is necessary to ensure not only that the functionality required of the composition is achieved but also that certain security properties are preserved. In this paper, we describe an approach to constructing secure service compositions. Our approach is based on the use of composition patterns and rules that determine the security properties that should be preserved by the individual services that constitute a composition in order to ensure that security properties of the overall composition are also satisfied. Our approach extends a framework developed to support the runtime service discovery
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A rule dynamics approach to event detection in Twitter with its application to sports and politics
The increasing popularity of Twitter as social network tool for opinion expression as well as informa- tion retrieval has resulted in the need to derive computational means to detect and track relevant top- ics/events in the network. The application of topic detection and tracking methods to tweets enable users to extract newsworthy content from the vast and somehow chaotic Twitter stream. In this paper, we ap- ply our technique named Transaction-based Rule Change Mining to extract newsworthy hashtag keywords present in tweets from two different domains namely; sports (The English FA Cup 2012) and politics (US Presidential Elections 2012 and Super Tuesday 2012). Noting the peculiar nature of event dynamics in these two domains, we apply different time-windows and update rates to each of the datasets in order to study their impact on performance. The performance effectiveness results reveal that our approach is able to accurately detect and track newsworthy content. In addition, the results show that the adaptation of the time-window exhibits better performance especially on the sports dataset, which can be attributed to the usually shorter duration of football events
The contribution of data mining to information science
The information explosion is a serious challenge for current information institutions. On the other hand, data mining, which is the search for valuable information in large volumes of data, is one of the solutions to face this challenge. In the past several years, data mining has made a significant contribution to the field of information science. This paper examines the impact of data mining by reviewing existing applications, including personalized environments, electronic commerce, and search engines. For these three types of application, how data mining can enhance their functions is discussed. The reader of this paper is expected to get an overview of the state of the art research associated with these applications. Furthermore, we identify the limitations of current work and raise several directions for future research
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