Data warehousing solutions work as information base for large organizations to support their decision making tasks.With the proven need of such solutions in current times, it is crucial to effectively design, implement and utilize these solutions.Data warehouse (DW) implementation has been a challenge for the organizations and the success rate of its implementation has been very low.To address these problems, we have proposed a framework for developing effective data warehousing solutions.The framework is primarily based on procedural aspect of data warehouse development and aims to standardize its process.We first identified its components and then worked on them in depth to come up with the framework for effective implementation of data warehousing projects.To verify effectiveness of the designed framework, we worked on National Rural Health Mission (NRHM) project of Indian government and designed data warehousing solution using the proposed framework.

Data modeling is an approach to model data by mapping operational tasks iteratively, while associated guidelines are either partly mapped in the data model or expressed through software applications.Since an organization is a collection of business processes, it is essential that data models utilize such processes to facilitate data modeling.Also, data models should incorporate guidelines for completing operational tasks through the concept of business rules.This paper outlines a unified framework on database modeling and design based on business process concepts that also incorporates business rules impacting business operations.The paper focuses on the relational database and its primary mode of conceptual modeling in the form of an entity relationship model.Concepts are illustrated through Oracle's database language PL/SQL and its Web variant PL/SQL Server Pages.

Personal networks are not always diverse or large enough to reach those with the right information. This problem increases when assembling a group of experts from around the world, something which is a challenge in Future-oriented Technology Analysis (FTA). In this work, we address the formation of a panel of experts, specifically how to select a group of experts from a huge group of people. We propose an approach which uses data quality dimensions to improve expert selection quality and provide quality metrics to the forecaster. We performed a case study and successfully showed that it is possible to use data quality methods to support the expert search process.

Ali Akbar Dehkhoda, the prominent lexicographer, describes a person who has difficulty in grasping knowledge as someone who "Cannot understand something without knowing all its details." If the knowledge required by somebody is in a language other than the person's mother tongue, access to this knowledge will surely meet special difficulties resulting from the person's lack of mastery over the second language. Any project that can monitor knowledge sources written in English and change them into the user's language by employing a simple understandable model is capable of being a knowledge-based project with a world view regarding text simplification. This article creates a knowledge system, investigates some algorithms for analyzing contents of complex texts, and presents solutions for changing such texts simple and understandable ones. Texts are automatically analyzed and their ambiguous points are identified by software, but it is the author or the human agent who makes decisions concerning omission of the ambiguities or correction of the texts.

Temporal Database is the most convenient form to represent time element associated with data. Temporal validity support a unique feature of temporal database lets you associate one or more valid time dimensions with a table and have data be visible depending on its time-based validity, as determined by the start and end dates or time stamps of the period for which a given record is considered to be a valid record. This study focuses on checking and verification of temporal data using valid time dimension of temporal database. This study covers the steps for adding a valid time dimension on a table, and various methods for querying the table and retrieving records based on a specified valid time value or range with help of Oracle 12c.

Personal networks are not always diverse or large enough to reach those with the right information.This problem increases when assembling a group of experts from around the world, something which is a challenge in Future-oriented Technology Analysis (FTA).In this work, we address the formation of a panel of experts, specifically how to select a group of experts from a huge group of people.We propose an approach which uses data quality dimensions to improve expert selection quality and provide quality metrics to the forecaster.We performed a case study and successfully showed that it is possible to use data quality methods to support the expert search process.

Nowadays, document clustering is considered as a data intensive task due to the dramatic, fast increase in the number of available documents. Nevertheless, the features that represent those documents are also too large. The most common method for representing documents is the vector space model, which represents document features as a bag of words and does not represent semantic relations between words. In this paper we introduce a distributed implementation for the bisecting k-means using MapReduce programming model. The aim behind our proposed implementation is to solve the problem of clustering intensive data documents. In addition, we propose integrating the WordNet ontology with bisecting k-means in order to utilize the semantic relations between words to enhance document clustering results. Our presented experimental results show that using lexical categories for nouns only enhances internal evaluation measures of document clustering; and decreases the documents features from thousands to tens features. Our experiments were conducted using Amazon Elastic MapReduce to deploy the Bisecting k-means algorithm.

The Internet is probably the most successful distributed computing system ever.However, our capabilities for data querying and manipulation on the internet are primordial at best.The user expectations are enhancing over the period of time along with increased amount of operational data past few decades.The data-user expects more deep, exact, and detailed results.Result retrieval for the user query is always relative o the pattern of data storage and index.In Information retrieval systems, tokenization is an integrals part whose prime objective is to identifying the token and their count.In this paper, we have proposed an effective tokenization approach which is based on training vector and result shows that efficiency/ effectiveness of proposed algorithm.Tokenization on documents helps to satisfy user's information need more precisely and reduced search sharply, is believed to be a part of information retrieval.Pre-processing of input document is an integral part of Tokenization, which involves preprocessing of documents and generates its respective tokens which is the basis of these tokens probabilistic IR generate its scoring and gives reduced search space.The comparative analysis is based on the two parameters; Number of Token generated, Pre-processing time.

Information is growing more rapidly on the World Wide Web (WWW) has made it necessary to make all this information not only available to people but also to the machines. Ontology and token are widely being used to add the semantics in data processing or information processing. A concept formally refers to the meaning of the specification which is encoded in a logic-based language, explicit means concepts, properties that specification is machine readable and also a conceptualization model how people think about things of a particular subject area. In modern scenario more ontologies has been developed on various different topics, results in an increased heterogeneity of entities among the ontologies. The concept integration becomes vital over last decade and a tool to minimize heterogeneity and empower the data processing. There are various techniques to integrate the concepts from different input sources, based on the semantic or syntactic match values. In this paper, an approach is proposed to integrate concept (Ontologies or Tokens) using edit distance or n-gram match values between pair of concept and concept frequency is used to dominate the integration process. The proposed techniques performance is compared with semantic similarity based integration techniques on quality parameters like Recall, Precision, F-Measure & integration efficiency over the different size of concepts. The analysis indicates that edit distance value based interaction outperformed n-gram integration and semantic similarity techniques.