Implementing Service Oriented Architecture for Data Mining

With Web technology, data on internet has become increasingly large and complex. No matter users or internet users needs all this data. Also the data which is available on web not all the time useful information or it is knowledgeable. Hence web data mining is necessary to fulfill this demand. Web data mining can extract unstructured, undiscovered data which is possibly useful information and knowledge, from much incomplete, noisy, ambiguous, random, practical application related data from WWW network. It is a new emerging commercial information/data mining technology. Its main characteristic is to extract key data to support business for decision making from business database through the use of extraction, conversion, analysis and other transaction models. Web service is deployed on the web with an object or component to achieve distributed application software platform through a series of protocols. Web Service platform provides a set of standard types systems, rules, techniques and internet service-oriented applications for communication between the different platforms, different programming languages and different types of systems to achieve interoperability. This paper gives the actual and practical application of web services for data mining, we build a data mining model based on Web services and going forward it is possible to implement the new data mining solution for security configuration. This has been achieved with the use of prototypes of a dynamic web service based data mining systems. DOI: 10.17762/ijritcc2321-8169.15079

Improved Data Mining Analysis by Dataset creation using Horizontal Aggregation and B+ Tree

Data Mining is one of the emerging field in Research and information retrieval. Data mining tools requires data in the form of data set. Data set preparation is one of the important task in data mining. Data set is collection of data which is stored in relational database where database schema are highly normal- ized. To analyze data efficiency, data mining systems are widely using datasets with columns in horizontal tabular layout. The two main components of sql code is join and aggregation Vertical aggregations have limitations to build data sets because they return one column for aggregated group using group functions. Preparing a data set for data mining analysis is generally the most tedious and time consuming task in a data mining project, which requires many complex SQL queries, joining tables and columns, and aggregating columns. A powerful methods to generate SQL code to return aggregated columns in a horizontal or cross tabular form, returning a set of numbers instead of one number per row is introduced. This new class of methods is called horizontal aggregations. Horizontal aggregations are evaluted using three functions : CASE, SPJ and PIVOT method.Data mining also deals with searching of information. This paper focuses on creation of B+ tree to reduce the time of information search so that efficiency of the system increases. DOI: 10.17762/ijritcc2321-8169.16045

Generic Framework for Gaining Insight Into Data

Efficient data analysis can be made easier with datasets having columns in horizontal tabular layout. Aggregations using standard SQL return one column per aggregated group. So existing SQL aggregations have limitations in preparing datasets. In this paper we have proposed a framework to build dataset using a new class of functions called horizontal aggregations. To speed up the dataset preparation task we have partitioned vertical aggregations on grouping column and optimized SPJ method. Also it is proposed to integrate summary dataset, obtained from the result of horizontal aggregation, into homogeneous cluster using K-means algorithm. DOI: 10.17762/ijritcc2321-8169.150616