Reviewed Study on Novel Search Mechanism for Web Mining

There are many methodologies for finding patterns in the client's navigation. For instance, acquaints new calculations with retrieve taxonomy of a solitary web webpage from the snap floods of its clients. They have developed a framework to discover how the time influences the client conduct while surfing a web page. That is, they segment the logs of navigation of the clients in various time intervals; and after that they find what time intervals truly meddle with the client conduct

Personalizing web search and crawling from clickstream data

Our aim is to improve web search engines, approaching the searching problem considering the user, his/her topics of interest and the navigation context. Furthermore, the clickstream also contains patterns inside. Our system will also try to predict the next pages that are going to be visited according to the clickstream. In a personalized search engine, two different users get different results for the same query, because the system considers the interests of each user separately. To personalize search, many sources of information can be used: the bookmarks of the user, his/her geographical location, his navigation history, etc. Web search engines have, broadly speaking, three basic phases. They are crawling, indexing and searching. The information available about the users interest can be considered in some of those three phases, depending on its nature. Work on search personalization already exists. We will see them in Chapter 3. In order to solve the problems of ignorance in relation to the user and his interests, we have developed a system that keeps track of the web pages that the user visits (his clickstream). Our system will analyze the clickstream, and will focus the crawling to pages related to the topics of interest of the user. Furthermore, each time the user executes a query, the system will consider his/her navigation context, and pages related to the navigation context will get better scores. Furthermore, our system also analyzes the clickstream of the user, and retrieves some navigation patterns from it. Those patterns will be used to give some navigation tips to the user based on his navigation context

Bookmark-driven query routing in peer-to-peer web search

Abstract: We consider the problem of collaborative Web search and query routing strategies in a peer-to-peer (P2P) environment. In our architecture every peer has a full-fledged search engine with a (thematically focused) crawler and a local index whose contents may be tailored to the user’s specific interest profile. Peers are autonomous and post meta-information about their bookmarks and index lists to a global directory, which is efficiently implemented in a decentralized manner using Chordstyle distributed hash tables. A query posed by one peer is first evaluated locally; if the result is unsatisfactory the query is forwarded to selected peers. These peers are chosen based on a benefit/cost measure where benefit reflects the thematic similarity of peers ’ interest profiles, derived from bookmarks, and cost captures estimated peer load and response time. The meta-information that is needed for making these query routing decisions is efficiently looked up in the global directory; it can also be cached and proactively disseminated for higher availability and reduced network load.

Bookmark-driven Query Routing in Peer-to-Peer Web Search

We consider the problem of collaborative Web search and query routing strategies in a peer-to-peer (P2P) environment. In our architecture every peer has a full-fledged search engine with a (thematically focused) crawler and a local index whose contents may be tailored to the user's specific interest profile. Peers are autonomous and post meta-information about their bookmarks and index lists to a global directory, which is efficiently implemented in a decentralized manner using Chord-style distributed hash tables. A query posed by one peer is first evaluated locally; if the result is unsatisfactory the query is forwarded to selected peers. These peers are chosen based on a benefit/cost measure where benefit reflects the thematic similarity of peers' interest profiles, derived from bookmarks, and cost captures estimated peer load and response time. The meta-information that is needed for making these query routing decisions is efficiently looked up in the global directory; it can also be cached and proactively disseminated for higher availability and reduced network load

Congenial Web Search : A Conceptual Framework for Personalized, Collaborative, and Social Peer-to-Peer Retrieval

Traditional information retrieval methods fail to address the fact that information consumption and production are social activities. Most Web search engines do not consider the social-cultural environment of users' information needs and the collaboration between users. This dissertation addresses a new search paradigm for Web information retrieval denoted as Congenial Web Search. It emphasizes personalization, collaboration, and socialization methods in order to improve effectiveness. The client-server architecture of Web search engines only allows the consumption of information. A peer-to-peer system architecture has been developed in this research to improve information seeking. Each user is involved in an interactive process to produce meta-information. Based on a personalization strategy on each peer, the user is supported to give explicit feedback for relevant documents. His information need is expressed by a query that is stored in a Peer Search Memory. On one hand, query-document associations are incorporated in a personalized ranking method for repeated information needs. The performance is shown in a known-item retrieval setting. On the other hand, explicit feedback of each user is useful to discover collaborative information needs. A new method for a controlled grouping of query terms, links, and users was developed to maintain Virtual Knowledge Communities. The quality of this grouping represents the effectiveness of grouped terms and links. Both strategies, personalization and collaboration, tackle the problem of a missing socialization among searchers. Finally, a concept for integrated information seeking was developed. This incorporates an integrated representation to improve effectiveness of information retrieval and information filtering. An integrated information retrieval process explores a virtual search network of Peer Search Memories in order to accomplish a reputation-based ranking. In addition, the community structure is considered by an integrated information filtering process. Both concepts have been evaluated and shown to have a better performance than traditional techniques. The methods presented in this dissertation offer the potential towards more transparency, and control of Web search

A Content-Addressable Network for Similarity Search in Metric Spaces

Because of the ongoing digital data explosion, more advanced search paradigms than the traditional exact match are needed for contentbased retrieval in huge and ever growing collections of data produced in application areas such as multimedia, molecular biology, marketing, computer-aided design and purchasing assistance. As the variety of data types is fast going towards creating a database utilized by people, the computer systems must be able to model human fundamental reasoning paradigms, which are naturally based on similarity. The ability to perceive similarities is crucial for recognition, classification, and learning, and it plays an important role in scientific discovery and creativity. Recently, the mathematical notion of metric space has become a useful abstraction of similarity and many similarity search indexes have been developed. In this thesis, we accept the metric space similarity paradigm and concentrate on the scalability issues. By exploiting computer networks and applying the Peer-to-Peer communication paradigms, we build a structured network of computers able to process similarity queries in parallel. Since no centralized entities are used, such architectures are fully scalable. Specifically, we propose a Peer-to-Peer system for similarity search in metric spaces called Metric Content-Addressable Network (MCAN) which is an extension of the well known Content-Addressable Network (CAN) used for hash lookup. A prototype implementation of MCAN was tested on real-life datasets of image features, protein symbols, and text — observed results are reported. We also compared the performance of MCAN with three other, recently proposed, distributed data structures for similarity search in metric spaces

Advanced methods for query routing in peer-to-peer information retrieval

One of the most challenging problems in peer-to-peer networks is query routing: effectively and efficiently identifying peers that can return high-quality local results for a given query. Existing methods from the areas of distributed information retrieval and metasearch engines do not adequately address the peculiarities of a peer-to-peer network. The main contributions of this thesis are as follows: 1. Methods for query routing that take into account the mutual overlap of different peers\u27; collections, 2. Methods for query routing that take into account the correlations between multiple terms, 3. Comparative evaluation of different query routing methods. Our experiments confirm the superiority of our novel query routing methods over the prior state-of-the-art, in particular in the context of peer-to-peer Web search.Eines der drängendsten Probleme in Peer-to-Peer-Netzwerken ist Query-Routing: das effektive und effiziente Identifizieren solcher Peers, die qualitativ hochwertige lokale Ergebnisse zu einer gegebenen Anfrage liefern können. Die bisher bekannten Verfahren aus dem Bereich der verteilten Informationssuche sowie der Metasuchmaschinen werden den Besonderheiten von Peer-to-Peer-Netzwerken nicht gerecht. Die Hautbeiträge dieser Arbeit teilen sich in folgende Schwerpunkte: 1. Query-Routing unter Berücksichtigung der gegenseitigen überlappung der Kollektionen verschiedener Peers, 2. Query-Routing unter Berücksichtigung der Korrelationen zwischen verschiedenen Termen, 3. Vergleichende Evaluierung verschiedener Methoden zum Query-Routing. Unsere Experimente bestätigen die Überlegenheit der in dieser Arbeit entwickelten Verfahren gegenüber den bisher bekannten Verfahren, insbesondere im Kontext von Peer-to-Peer-Websuche

Approximate information filtering in structured peer-to-peer networks

Today';s content providers are naturally distributed and produce large amounts of information every day, making peer-to-peer data management a promising approach offering scalability, adaptivity to dynamics, and failure resilience. In such systems, subscribing with a continuous query is of equal importance as one-time querying since it allows the user to cope with the high rate of information production and avoid the cognitive overload of repeated searches. In the information filtering setting users specify continuous queries, thus subscribing to newly appearing documents satisfying the query conditions. Contrary to existing approaches providing exact information filtering functionality, this doctoral thesis introduces the concept of approximate information filtering, where users subscribe to only a few selected sources most likely to satisfy their information demand. This way, efficiency and scalability are enhanced by trading a small reduction in recall for lower message traffic. This thesis contains the following contributions: (i) the first architecture to support approximate information filtering in structured peer-to-peer networks, (ii) novel strategies to select the most appropriate publishers by taking into account correlations among keywords, (iii) a prototype implementation for approximate information retrieval and filtering, and (iv) a digital library use case to demonstrate the integration of retrieval and filtering in a unified system.Heutige Content-Anbieter sind verteilt und produzieren riesige Mengen an Daten jeden Tag. Daher wird die Datenhaltung in Peer-to-Peer Netzen zu einem vielversprechenden Ansatz, der Skalierbarkeit, Anpassbarkeit an Dynamik und Ausfallsicherheit bietet. Für solche Systeme besitzt das Abonnieren mit Daueranfragen die gleiche Wichtigkeit wie einmalige Anfragen, da dies dem Nutzer erlaubt, mit der hohen Datenrate umzugehen und gleichzeitig die Überlastung durch erneutes Suchen verhindert. Im Information Filtering Szenario legen Nutzer Daueranfragen fest und abonnieren dadurch neue Dokumente, die die Anfrage erfüllen. Im Gegensatz zu vorhandenen Ansätzen für exaktes Information Filtering führt diese Doktorarbeit das Konzept von approximativem Information Filtering ein. Ein Nutzer abonniert nur wenige ausgewählte Quellen, die am ehesten die Anfrage erfüllen werden. Effizienz und Skalierbarkeit werden verbessert, indem Recall gegen einen geringeren Nachrichtenverkehr eingetauscht wird. Diese Arbeit beinhaltet folgende Beiträge: (i) die erste Architektur für approximatives Information Filtering in strukturierten Peer-to-Peer Netzen, (ii) Strategien zur Wahl der besten Anbieter unter Berücksichtigung von Schlüsselwörter-Korrelationen, (iii) ein Prototyp, der approximatives Information Retrieval und Filtering realisiert und (iv) ein Anwendungsfall für Digitale Bibliotheken, der beide Funktionalitäten in einem vereinten System aufzeigt