Himanshu Aggarwal

Service Oriented Architecture Adoption Trends: A Critical Survey

Analyst reports are confirming that adoption of SOA is growing; the actual goal of SOA is to help align IT capabilities with business goals. In today’s competitive scenario where business demand changes very frequently, the expectation from technology is raised to level where we are expecting the business processes are developed in such a manner that they can adapt the frequent changes without affecting the overall organization business architecture. Thus the need to assume business processes as a smart services that can be loosely coupled. Thus need of service oriented architecture arises. This paper is a review of articles and research work that have undergone in the past 1 decade (i.e. from 2001 - 2011). The source of data is from most prestigious journal and website covering area of SOA, In this paper we have identified the factors that are relevant to SOA implementation and up to how much extents each factor is crucial to SOA implementation is also identified.

Web Log Analysis for Identifying the Number of Visitors and their Behavior to Enhance the Accessibility and Usability of Website

Web usage mining is crucial for the CustomerRelationship Management (CRM) as it can ensure customer satisfaction as far as the interaction between the customer and the organization is concerned. Web usage mining is also helpful for identifying or improving the visitors of a particular Website by accessing the log file of that site. In this paper the focus is on Web usage mining of Log data of an educational institution . Web logExpert Lite 8.6 tool has been used for analysis results are shown. General Terms Web Log Analyzer, Web Log,Web Usage Mining.

Improving Customer Relationship Management Using Data Mining

Customer Relationship Management (CRM) refers to the methodologies and tools that help businesses manage customer relationships in an organized way. Data Mining is the process that uses a variety of data analysis and modeling techniques to discover patterns and relationships in data that may be used to make accurate predictions. It can help you to select the right prospects on whom to focus. The essence of the information technology revolution and, in particular, the World Wide Web is the opportunity to build better relationships with customers than has been previously possible in the offline world. For instance, as part of their CRM strategy, a business might use a database of customer information to help construct a customer satisfaction survey, or decide which new product their customers might be interested in.

A Change Model Based on an Integrated Approach For Strategic Business Information Technology Alignment For Sustainable Competitive Advantage

Strategic Alignment is a leading principle both for the research programs and for the practical methods dealing with the Business-Information Technology (IT) relationship. This enables a firm to maximize its value for the IT investments, achieve harmony with its business strategies and plans, leading to greater profitability. But, a great deal of uncertainty continues to surround the question of payoffs from the information technology investment despite the fact that the business executives have ranked consistently the strategic alignment of information Technology strategy with business as of prime importance. An issue that prevails is the inability to realize the sufficient value for the IT investment seems due in part is absence of strategic alignment. In this paper a model of change has been proposed that integrates resource/architectural, capability and the strategy levels. The main theme of the research is to achieve strategic alignment identifying various factors in these levels.

A graph-based model to improve social trust and influence for social recommendation

Social big data is large scale of data due to exponential popularity of social network and social media. Researchers can use social big data and social network for their observations if they analyse those in an intelligent manner. The target of intelligent decision is to find the most credible user in social network, who has the highest influence. A very large number of users are connected in social networks, implicitly friends-of-friends or explicitly mutual friends. They are able to communicate with each other and share their likes or dislikes on different topics. If users want to analyse any topic or purchase product like movie, book, they are populated with a lot of choices. Information overload due to large number of choices available to users limits effective product selection and hence results in reduced users’ satisfaction. Recommendation models are solution for providing better suggestion to users. Product’s recommendation at Amazon, friend’s recommendation at Facebook and music recommendation at iTunes are some of the popular examples of suggestions made on the basis of user’s interests. Recommendation models ease the user by reducing search space in social network graph. The main purpose of this paper is to improve social recommendations so that better and more appropriate choices are available for users. In this paper, an efficient technique for social recommendations using hyperedge and transitive closure is proposed. Social big data is processed and analysed in the form of social graphs. User–user and user–item connections are represented in the form of matrices. We have exploited homophily so that large number of connected users have trust on each other. Our model provides better recommendation to users by leveraging increased trust. The proposed model overcomes the limitations of traditional recommender systems like sparsity, cold start. It also improves prediction accuracy. The proposed model is evaluated through different metrics like MAE, precision, recall and RMSE. Empirical analysis shows significant improvement in recommendations. We have used Mahout library for improving recommendation accuracy and also handling large volume of data. SNAP library is also used for analysis of social big graphs. The proposed recommendation model is evaluated using Epinions and FilmTrust datasets. These datasets contain user’s ratings for various products in the scale of 1–5. Through analysis it is verified that the proposed model boosts the performance significantly. We have formulated recommendation model using manipulated social graph as per our proposed technique. This manipulated graph is mentioned as influence product graph (IPG) throughout this paper. IPG increases social trust value between connected users and this effect in recommending products in an effective and efficient manner.

A Novel Semantically-Time-Referrer based Approach of Web Usage Mining for Improved Sessionization in Pre-Processing of Web Log

Web usage mining(WUM) , also known as Web Log Mining is the application of Data Mining techniques, which are applied on large volume of data to extract useful and interesting user behaviour patterns from web logs, in order to improve web based applications. This paper aims to improve the data discovery by mining the usage data from log files. In this paper the work is done in three phases. First and second phase0 which are data cleaning and user identification respectively are completed using traditional methods. The third phase, session identification is done using three different methods. The main focus of this paper is on sessionization of log file which is a critical step for extracting usage patterns. The proposed referrer-time and Semantically-time-referrer methods overcome the limitations of traditional methods. The main advantage of pre-processing model presented in this paper over other methods is that it can process text or excel log file of any format. The experiments are performed on three different log files which indicate that the proposed semantically-time-referrer based heuristic approach achieves better results than the traditional time and Referrer-time based methods. The proposed methods are not complex to use. Web log file is collected from different servers and contains the public information of visitors. In addition, this paper also discusses different types of web log formats.

Big data analytics framework to identify crop disease and recommendation a solution

Due to technology the term data is replaced by transforming big data in many fields. Rapidly advancements in the technology causes agricultural data enter into the era of big data. Traditional tools and techniques are unable to store and analyze this massive amount of data. To store and analyze this type of data parallel computing and analyze paradigm is required. Big data analytic is used as a solution to this. In the paper big data analytic Agriculture framework is developed that identify disease based on symptoms similarity and recommend a solution based on high similarity. To achieve this objective Hadoop and Hive tools has been used. The data is collected, cleansed and normalized. Data is collected from laboratory reports, web sites etc. then cleansing of data is done that is important information is extracted from unstructured redundant data. In the next step normalization is done that is features are extracted from cleaned data. Normalized data is uploaded on HDFS and save in a file supported by hive. HiveQL is a SQL like query language and used to analyze the agricultural data. It finds out disease name based on crop disease symptoms and purpose a solution based on evidence from historical data. Result is represented in form of graphs that will useful for recommending a solution that is highly used or high symptoms similarity.

Segmentation of retail customers based on cluster analysis in building successful CRM

Direct marketers uses data mining technique called segmentation based on cluster analysis to target a subset of their customers for improving their profits. As the world is growing more and more competitive, the customer need and experience is becoming more important to the businesses. CRM based on data mining is a comprehensive strategy and a process of acquiring, retaining, and partnering with selective customers to create superior value for the business by using customer knowledge. The objective of the paper is to segment the relevant customers through cluster analysis that may be helpful to the marketers in increasing their profit, sales and building long-term relationships with them. Segmentation of the customers can be achieved through cluster analysis. The findings in the paper may help the retailers to focus those segments of customers that increase their business, sales and profit and also aid in customer build ups and maintaining long-term relationships.

HL-7 Based Middleware Standard for Healthcare Information System: FHIR

Fast health Interoperability Resources (FHIR), a advanced proposed emerging standard of Health Level 7 (HL7) that inheritance various advantageous of HL7- v2 and v3 for providing health interoperability. HL7 messaging Standard has been widely implemented and adopted by healthcare domain internationally from last few decades. Among hospital HL7-V2 (version ‘v2’) is preferred choice as compared to standard v3 to exchange healthcare information like electronic health records (EHR) among local hospitals. HL7-V3 was successor of the HL7-V2 that inherits various features and overcome various shortcomings of the V2. HL7-V3 standard had been highly criticized by the healthcare industry due to various shortcomings like complex documentation, implementation and maintenance cost high along with stalled system. HL7 standards has been introduced new approach FHIR standard which yet under experimental stage. FHIR has various attractive features like user friendly features, various built in modules and widely compatible with existing web standards. This research paper will provide substantiation evolution of the HL-7 standards pattern messaging, prologue related to the HL7 FHIR and comparison among HL7 standards.

Mobile based application for prediction of diabetes mellitus: FHIR Standard

Presently physicians providing clinical decision along clinical guidelines manually on basis of manual query to patients that involve documentation files of lab reports, medication description etc., that make practice sluggish and arduous. With development of mobile based application provide assistance to physician in providing quick decision in appropriate manner as patient data available any-time from any-where that will enhance quality of care in field of healthcare. HL7 standard promotes FHIR(Fast Health Interoperability Resource) standard and health information technology for mobile-health. Mobile health is not a vertical field, but a kind of horizontal field that has cut various impact of healthcare domains. This application provide the effective health management of patients electronic health record with ease of user friendly interface, that can be accessed on role based authentication by numbers of actors like patient, physician, clinic staff and patients family etc. Basically this study projected decision support system for prediction of chronic disease-DiabeticMellitus using FHIR(Fast Health Interoperability Resource) as standard for interoperability among hospital information system. Application provide physicians structured series of symptoms/signs as uploaded by patients, laboratory and staff. Series of questioner along with outcomes are formed, all outcomes appraised and tested by team of medical domain experts.