817 research outputs found

    WAQS : a web-based approximate query system

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    The Web is often viewed as a gigantic database holding vast stores of information and provides ubiquitous accessibility to end-users. Since its inception, the Internet has experienced explosive growth both in the number of users and the amount of content available on it. However, searching for information on the Web has become increasingly difficult. Although query languages have long been part of database management systems, the standard query language being the Structural Query Language is not suitable for the Web content retrieval. In this dissertation, a new technique for document retrieval on the Web is presented. This technique is designed to allow a detailed retrieval and hence reduce the amount of matches returned by typical search engines. The main objective of this technique is to allow the query to be based on not just keywords but also the location of the keywords within the logical structure of a document. In addition, the technique also provides approximate search capabilities based on the notion of Distance and Variable Length Don\u27t Cares. The proposed techniques have been implemented in a system, called Web-Based Approximate Query System, which contains an SQL-like query language called Web-Based Approximate Query Language. Web-Based Approximate Query Language has also been integrated with EnviroDaemon, an environmental domain specific search engine. It provides EnviroDaemon with more detailed searching capabilities than just keyword-based search. Implementation details, technical results and future work are presented in this dissertation

    Analyzing international travelers\u27 profile with self-organizing maps

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    It is generally agreed that knowledge is the most valuable asset to an organization. Knowledge enables a business to effectively compete with its competitors. In the tourism context, an in-depth knowledge of the profile of international travelers to a destination has become a crucial factor for decision makers to formulate their business strategies and better serve their customers. In this research, a self-organizing map (SOM) network was used for segmenting international travelers to Hong Kong, a major travel destination in Asia. An association rules discovery algorithm is then utilized to automatically characterize the profile of each segment. The resulting maps serve as a visual analysis tool for tourism managers to better understand the characteristics, motivations, and behaviors of international travelers

    Processing, analysis and recommendation of location data

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    Finding usage patterns from generalized weblog data

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    Buried in the enormous, heterogeneous and distributed information, contained in the web server access logs, is knowledge with great potential value. As websites continue to grow in number and complexity, web usage mining systems face two significant challenges - scalability and accuracy. This thesis develops a web data generalization technique and incorporates it into the web usage mining framework in an attempt to exploit this information-rich source of data for effective and efficient pattern discovery. Given a concept hierarchy on the web pages, generalization replaces actual page-clicks with their general concepts. Existing methods do this by taking a level-based cut through the concept hierarchy. This adversely affects the quality of mined patterns since, depending on the depth of the chosen level, either significant pages of user interests get coalesced, or many insignificant concepts are retained. We present a usage driven concept ascension algorithm, which only preserves significant items, possibly at different levels in the hierarchy. Concept usage is estimated using a small stratified sample of the large weblog data. A usage threshold is then used to define the nodes to be pruned in the hierarchy for generalization. Our experiments on large real weblog data demonstrate improved performance in terms of quality and computation time of the pattern discovery process. Our algorithm yields an effective and scalable tool for web usage mining

    Towards a characteristics-aware document search engine

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    The increasing volume, heterogeneity, and redundancy of the Web create a novel challenge for search engines in which, target documents must satisfy some characteristics. It is increasingly important because there are more and more types of web pages on the Internet nowadays. Current web search engines are fundamentally incapable of addressing the user need because keywords can not express characteristics of target pages. Another alternative is to use vertical search engines, but they can only cover a few popular niches. Thus, we propose Forward Search to empower users with an engine to express not only topics but also characteristics of pages in their queries. Expected results are documents ranked by both topical and characteristic relevance. Creating Forward Search to have the focus of a vertical engine and the flexibility of web search presents many novel challenges. First, we must represent a document with novel information to support querying for characteristics. Second, we must index both keywords and named entities to quickly locate relevant pages of the target characteristics during query time. Third, we have to design a realistic method to acquire user input about the characteristics of target pages and retrieve documents ranked by both topical and characteristic relevance. Finally, since our system redefines relevance in traditional web search, we must rethink the user interface. This thesis comes with a web-based demonstration of our Forward Search at http://crow.cs.illinois.edu:8080 and five open-source code repositories at https://github.com/forward-uiuc

    Principles and Applications of Data Science

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    Data science is an emerging multidisciplinary field which lies at the intersection of computer science, statistics, and mathematics, with different applications and related to data mining, deep learning, and big data. This Special Issue on “Principles and Applications of Data Science” focuses on the latest developments in the theories, techniques, and applications of data science. The topics include data cleansing, data mining, machine learning, deep learning, and the applications of medical and healthcare, as well as social media

    Automated classification of web contents in B2B marketing

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    Recent growth in digitization has affected how customers seek the information they need to make a purchase decision. This trend of customers making their purchase decision based on the information they collect online is increasing. To accommodate this change in purchase behavior, companies tend to share as much information about themselves and their products online, which in turn drives the amount of unstructured data produced. To get value for this huge amount of data being produced, the unstructured data needs to be processed before being used in digital marketing applications. When it comes to the companies serving business to customers (B2C), plenty of research exists on how the digital content could be used for marketing, but for the companies serving business to business (B2B) a huge research gap presides. B2C marketing and B2B marketing might share some analytical concepts but they are different domains. Not much research has been done in the field of using machine learning in B2B digital marketing. The lack of availability of labeled text data from the B2B domain makes it challenging for researchers to experiment on text classification models, while several methods have been proposed and used to classify unstructured text data in marketing and other domains. This thesis studies previous works done in the field of text classification in general, in the marketing domain, and compares those methods across the dataset available for this research. Text classification methods such as Random Forest, Linear SVM, KNN, Multinomial Naïve Bayes, and Multinomial Logistic Regression dominates the research field, hence these methods are tested in this research. In the used dataset surprisingly, Random Forest Classifier performed best with an average accuracy of 0.85 in the designed five-class classification task

    Personalizing Interactions with Information Systems

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    Personalization constitutes the mechanisms and technologies necessary to customize information access to the end-user. It can be defined as the automatic adjustment of information content, structure, and presentation tailored to the individual. In this chapter, we study personalization from the viewpoint of personalizing interaction. The survey covers mechanisms for information-finding on the web, advanced information retrieval systems, dialog-based applications, and mobile access paradigms. Specific emphasis is placed on studying how users interact with an information system and how the system can encourage and foster interaction. This helps bring out the role of the personalization system as a facilitator which reconciles the user’s mental model with the underlying information system’s organization. Three tiers of personalization systems are presented, paying careful attention to interaction considerations. These tiers show how progressive levels of sophistication in interaction can be achieved. The chapter also surveys systems support technologies and niche application domains
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