Presentation on theme: "Retrieval of Information from Distributed Databases By Ananth Anandhakrishnan."— Presentation transcript:
Retrieval of Information from Distributed Databases By Ananth Anandhakrishnan
Outline Introduction to Distributed Computer Systems The need for Distributed IR Distributed IR Problems of Distributed IR - system components Federated search engine Other examples Distributed IR Conclusion
Introduction to Distributed Computer Systems What is it? A distributed system is a collection of independent computers that appears to its users as a single coherent system. The first Distributed System: IBM 1961 develop a Compatible Time Sharing System. More recent times The WWW concept was designed in 1989 at CERN. wide spread use in the 90's. 1972 ARPANET - building blocks of the INTERNET INTERNET - network or networks
Other types of Distributed systems ORACLE – Distributed Database Management System Air Traffic Control System – Real-time Distributed System University Network - Client-Server system Question: is a search engines a distributed system? Yes - single interface - search engines like Google have a cluster of 4000 computers doing its web crawling. No - user is aware of where searched documents come from. web address (URL) - Google's control is centralised - index and presentation Goals Share and access resource located on remote sites Scalability Transparency Fault Tolerance
The need for Distributed IR Benefits of Centralised IR Centralised control of resources is easier to manage More relevant resources are selected from user query on a centralised system Why is there a need for Distributed IR? Problems of centralised systems It’s not scalable for millions of users accessing single server - Increases network traffic - Increases server load There is a single point of failure Problems in IR Information is constantly growing. Different types of information are emerging with different formats and standards, residing on heterogeneous networks. hard to integrate services.
The need for Distributed IR Improves Scalability Distribute the information to a network of servers. Apply Standards and Protocols Z39.50 search protocol - allows a uniform access to a large number of diverse and heterogeneous information sources. client server computing. Dublin Core - standards applied to metadata (data about data) to make searching for information more efficient. Replication model for information retrieval Removes single point of failure Improves scalability issues
Distributed IR What is distributed information retrieval? The goal of distributed information retrieval is to enable the identification and retrieval of data sets relevant to a general description or query, wherever those data sets may be located or hosted USERAPPLICATIONDISTRIBUTED DATABSES Environments: cooperative or uncooperative
Distributed IR How does it works? Library Example library organisation has sites in different locations and has different internet accessible resources (e-journals, e-books) in different categories (literature, science, computing, geography, history, sports). Each library maintains its own database, with the resources, a unique identifier for the resources and detailed descriptions of the resources, and statistical information about the resource content. each library may have resources of the same type The library organisation has an online search engine, which enables users to search for any online resource in any category in all the libraries databases. Example of query User enters a query into the search application which will pass this request to all the individual library databases. These databases will return a list of unique identifiers of the relevant resources which are merged together in the application to present to the single ranked list to the user. If user finds a resource they want to view, the resource identifier is used to retrieve the resource.
Problems of Distributed IR - system sub components main components resource description resource selection query translation resource merging Resource description database files which contain detailed information about the resources. cooperative environments - START protocol uncooperative environments - Query based sampling Dublin Core - standards used to improve indexing information for resource descriptions. 15 elements - used to uniquely identify information or resources. Embedded into XML or HTML Example TITLE: Information Retrieval from Distributed Databases CREATOR: Ananth Anandhakrishnan DATE: 24-11-2004 FORMAT: WORD DOCUMENT LANGUAGE: ENGLISH
Dublin Core Metadata in HTML and XML Distributed Information Retrieval <meta name = "DC.Title" content = " Retrieval of information from DIR "> <meta name = "DC.Creator" content = "Ananth Anandhakrishnan"> <meta name = "DC.Date" content = "24/11/2004"> <meta name = "DC.Format" content = "text/html"> <meta name = "DC.Language" content = "en"> </body HTML has a tag called META XML embedded with a framework RDF <rdf:RDF Ananth Anandhakrishnan Distributed Information Retreival How does Information Retrieval from Distributed databasesworks 2004-11-24
Resource Selection Component Resource Selection two jobs: involves identifying a small set of databases from the distributed information retrieval system that contains documents relevant to a query. after databases are selected a ranked list is produced This Process based on using algorithms CORI KL Divergence Relevant Document Distribution Estimation (ReDDE) Which is the best? ReDDE is proven to be the best algorithm for resource selection. estimates the distribution of relevant documents across the databases for each user query and ranks databases according to this distribution of relevant documents.
Resource Merging Result Merging Selected resources are complied into a single result. removes any duplication of resources Problems different databases use different selection algorithms difficult to merge. solution use standard selection algorithms more problems current merging methods take place at client end - isolated from DIR current methods are not very good. round robin - selecting the first database that it hits, doesn’t take into account of its relevance raw merge - results based on document scores solution place merging component near the selection component Semi Supervised Learning model - resource merging method. aim: produce a ranked list which is similar to one of a centralised information retrieval system. achieved: running a centralised sample database in parallel with the distributed databases. centralised sample database - using query based sampling to build resource descriptions.
Ranked list document links Semi Supervised Learning Model Query entry Resource selection Merging results CENTRALISED SAMPLE DATABASE DISTRIBUTED DATABASES Resource Descriptions of documents held on all databases. Obtained by querying Query is sent to a centralised sample database Merged results ranked by relevance. Combine document ranking Merged list Ranked list of documents from central database. Individual ranked lists Database independent scores Database specific scores
Semi Supervised Learning Model How distributed information retrieval works in more detail A user enters a query The query is used to rank the collection of databases from which a set of databases are selected. The query is then broadcasted to all the selected databases from which it produces a ranked list of all matches with document id and scores. The document ids and scores are added to the merging algorithm. The query is also broadcasted to the parallel running centralized database and the ranked list of document id’s and scores are also inputted into the merging algorithm. The ranked list provided by the central database will influence the resources merged from the distributed databases. SSL The SSL algorithm specifically models result merging as a task of transforming sets of database-specific document scores into a single set of database-independent document scores by using the documents acquired by query-based sampling as training data. Uses a regression algorithm to do this.
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We can also filter results by specific database. This is especially helpful in identifying particular information, such as patent data, within the results list.
Other Examples Emerge Emerge is a software built for information retrieval of scientific data. makes use of the Dublin core and Z39.50 search protocol XML-based translation engine which can perform metadata mapping and query translation. Harvest collects information from : - internet, intranet using http, ftp - local files like data on hard disk, CDROM and file servers. makes them searchable using a web interface supports wide range of formats Summary Object Interchange Format (SOIF) - metadata mapping BrokerGathererProvider 2 Provider 1 Provider 3 Client Collects information available at provider Collects, stores and managers the information for clients to query
Other Examples User information to keep track of processing data SETI@Home SETI@Home is a screensaver program used to aid the search for extraterrestrial life uses client computers CPU power to process data packets.
Conclusion Distributed Computing Concepts help information retrieval systems Distributed IR depends on Centralised IR - tries to emulate it Current State of Distributed Search GRUB screensaver program which uses your bandwidth and CPU power produces the most up-to-date indexes. have not got wide level of support. P2P search well known for Napster and Kazaa more dynamic than Google- allows users to upload whatever they want, and make it search available Google is in a controlled environment. not considered in commercial field - they don’t see the benefits.