Informatics Publishing Limited

S. T. Padmapriya ¹, J. Deepika ¹

Affiliations
1 Department of Computer Science and Engineering, Sethu Institute of Technology, Virudhunagar-626115, Tamilnadu, IN

Abstract

Continuous queries are used to monitor changes to time varying data and to provide results useful for online decision making. Typically a user desires to obtain the value of some aggregation function over distributed data items, for example, to know value of portfolio for a client; or the AVG of temperatures sensed by a set of sensors. Resource utilized group aggregation for correlated type validation at client level is the proposed access. Correlated sub query is the query to return the resultant records in partial manner. It means, the query evaluates grouped records and fetches the result until the last group is met. In large data bases there are highly maximized records to meet the groups. This type of query evaluates once and returned to result to parent and continues to fetch the retrieval process. In this paper, Client Framework prepares task and it will be divided into many divisions to start separate way to start the multi connection process using parallel approach. Intelligent Group Service checks the availability of groups and decides percentage level based on the idle resources in network. Percentage tasks decide how many groups can be sent to one available server. The parallel approach starts the retrieval process from the system based on the percentage. After the downloading process, the assembler starts the assembling process. Data producer Service prepares the result and return to Client frame work. After making fast retrieval Assembler assembles it in locally. Population Data are replicated in Mirror Network. Client system finds the availability of available servers and makes the communication in dynamic level. Group based segregated process against servers is started from client process by IGS.

Keywords

Algorithms, Continuous Queries, Distributed Query Processing, Data Dissemination, Coherency, Performance, Parallel Way Source.

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J. Deepika ¹, S. Senthamarai Kannan ¹

Affiliations
1 Sethu Institute of Technology, IN

Abstract

In real time applications, identification of records that represent the same real-world entity is a major challenge to be solved. Detection and removal of duplicate records that relate to the same entity within one dataset is an important task in data preprocessing. The novel data extraction and alignment method called CTVS that combines both tag and value similarity is enhanced by using unsupervised duplicate detection algorithm (UDD) to eliminate the duplicate records in web databases. CTVS automatically extracts data from query result pages by first identifying and segmenting the query result records (QRRs) in the query result pages and then aligning the segmented QRRs into a table, in which the data values from the same attribute are put into the same column. Specifically, new techniques are proposed to handle the case when the QRRs are not contiguous, which may be due to the presence of auxiliary information, such as a comment, recommendation or advertisement, and for handling any nested structure that may exist in the QRRs. Also a new record alignment algorithm that aligns the attributes in a record, first pairwise and then holistically, by combining the tag and data value similarity information is designed.

Keywords

Automatic Wrapper Generation, Data Extraction, Data Record Alignment, Duplicate Detection.

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