The e-Market Integrator is a Business to Business (b2b) Application designed to assiste in the process of Supply Chain Management. This is a J2EE Application, which makes use of the evolving Web Services standards to create an application, which integrates different system and allows them to work cooperatively to perform a product search. The technique discussed here resembles a b2c-shopping portal where Internet surfers key in a product name (which they are looking for); the site returns product details from many different online retailers. The major difference in this case is that even the customer end is going to be an automated process and data has to be exchanged in a format which is machine understandable. This is a prototype application, which aims to prove how J2EE along with new Open Source Libraries and Web Sphere Technology can be used to develop and deploy a solution to such a problem.

Numerous graph-based frequent pattern mining algorithms have been developed in the past. Identifying frequent structures involves locating sub graphs that occur frequently over the entire set of graphs. In this paper an experimental comparison of two graph-based algorithms for identifying frequent structures in semi-structured data has been presented and discussed in this paper. The SEuS algorithm for recognizing frequent structures has been explored and the results are compared with the SUBDUE algorithm. An analysis of the experimental results indicates that pin pointing frequent structures in a huge graph using SEuS algorithm is faster compared to the SUBDUE algorithm. The computational complexity oflocating frequent structures in SEuS algorithm has been overcome using a summary data structure which prunes the search space. � 2009 ISSN.

The robust networking environments and technology makes it difficult for the analysis and debugging of network problems. There are many network testing tools that capture and replay the packets to help in debugging. But the tools that create an network environment by synchronously simulating the captured packets between two hosts which also has other features like filter, modify and save options make this tool unique. � Research India Publications.

The e-Market Integrator is a Business to Business (b2b) Application designed to assists in the process of Supply Chain Management. This is a J2EE Application, which makes use of the evolving Web Services standards to create an application, which integrates different system and allows them to work cooperatively to perform a product search. The technique discussed here resembles a b2c-shopping portal where Internet surfers key in a product name (which they are looking for); the site returns product details from many different online retailers. The major difference in this case is that even the customer end is going to be an automated process and data has to be exchanged in a format, which is machine understandable. This is a prototype application, which aims to prove how J2EE along with new Open Source Libraries and Web Sphere Technology can be used to develop and deploy a solution to such a problem.

In an on demand business, information technology, IT, components produced by high-tech companies over the past decades are so complex that IT professionals are challenged to effectively operate a stable IT infrastructure. It is the complexity of the IT components themselves that have helped fuel this problem. An exponential growth has occurred in the number and variety of systems and components. The value of database technology and the Internet have fueled significant growth in storage subsystems, which now are capable of holding terabytes of structured and unstructured information. Our information society has created unpredictable and highly variable workloads for these networked systems. And these increasingly valuable, complex systems require highly skilled IT professionals to install, configure, operate, tune and maintain them. In this survey paper, we discuss about autonomic Computing, self optimizing systems and its components with respect to DBMS and future work to be carried out in this area. The difficult and complex tasks are performed through autonomic way with low cost, reduction of labor and in an efficient manner. ICIC International �2011 ISSN.

Web Crawler is a software, which crawls through the WWW to build database for a search engine. In recent years, web crawling has started facing many challenges. Firstly, the web pages are highly unstructured which makes it difficult to maintain a generic schema for storage. Secondly, the WWW is too huge and it is impossible to index it as it is. Finally, the most difficult challenge is to crawl the deep web. Here we are proposing a novel web crawler, which uses Neo4J, HBase as data stores. It also applies Natural Language Processing (NLP) and machine learning techniques to resolve the above-mentioned problems. � Research India Publications.

This paper proposed efficient and reliable algorithm for background subtraction for indoor video sequences. The paper mainly focused on compensating the illumination variation in the frame and then applying improved Gaussian mixture model to build the background model and then detect the moving foreign objects in indoor video sequences by using the Euclidian distance as metric. To compensate the illumination variation, homomorphic filtering algorithm for color image in HSV color space was proposed in the paper. The paper also reported the performance evaluation of the proposed method and existing and found that the proposed algorithm achieved 80 % of improvement in detecting actual foreground objects in indoor video sequences having illumination variations. � Springer Science+Business Media Singapore 2017.

A key motivator for the usage of cryptocurrency such as bitcoin in illicit activity is the degree of anonymity provided by the alphanumeric addresses used in transactions. This however does not mean that anonymity is built into the system as the transactions being made are still subject to the human element. Additionally, there is around 400 Gigabytes of raw data available in the bitcoin blockchain, making it a big data problem. HPCC Systems is used in this research, which is a data intensive, open source, big data platform. This paper attempts to use timing data produced by taking the time intervals between consecutive transactions performed by an address and make an identification of the nature of the address (illegal or legal).With the use of three different goodness of fit run tests namely Kolomogorov-Smirnov test, Anderson-Darling test and Cram�r-von Mises criterion, two addresses are compared to find if they are from the same source. The BABD-13 dataset was used as a source of illegal addresses, which provided both references and test data points. The research shows that time-series data can be used to represent transactional behaviour of a user and the algorithm proposed is able to identify different addresses originating from the same user or users engaging in similar activity � 2023, International Journal of Advanced Computer Science and Applications.All Rights Reserved.

A great interest has been fueled in object tracking solutions, mainly because of factors such as an increase in the number of powerful computers, the availability of high-quality video cameras, and the need for automation in video analysis. Tracking plays a vital role in various application domains, such as traffic monitoring, object recognition, autonomous surveillance systems, defence etc. This paper conducts a concise review on some of the few important techniques that are being used for robust tracking of moving objects in videos, completely along with their merits and demerits. Also, a thorough performance analysis of popular techniques - Mean Shift, CamShift, Optical Flow, Kalman Filter and SURF - has been performed, and the results have been interpreted and illustrated. � 2018 IEEE.

The advent of online news outlets and the recent explosion in number of users who consume news using this medium has led to several websites competing to grab users' attention. This has led to outlets creating creative ways to lure to reader to their website. One of the most widely used techniques is employing 'clickbait' headlines. These headlines are specifically designed to pique the reader's interest in what is promised when the article is clicked on. The article usually does not deliver the content the reader is looking for. In this work we aim to create a model to detect such headlines. We analyse what constitutes as clickbait and extract features which may be indicative of clickbait. The features extracted from the article are both syntactic and semantic and encompass the variability that is found in such headlines. Finally we build a classifier that can distinguish between actual news and what is likely to be clickbait. We use multiple classification algorithms and weigh the advantages presented by them. � 2017 IEEE.