The world has been drowned by floods of data due to technological development. Consequently, the Big Data term has gotten the expression to portray the gigantic sum. Different sorts of quick data are doubling every second. We have to profit from this enormous surge of data to convert it to knowledge. Knowledge Discovery (KDD) can enhance detecting the value of Big Data based on some techniques and technologies like Hadoop, MapReduce, and NoSQL. The use of Big Data value is critical in different fields.
This survey discusses the expansion of data that led the world to Big Data expression. Big Data has distinctive characteristics as volume, variety, velocity, value, veracity, variability, viscosity, virality, ambiguity, and complexity. We will describe the connection between Big Data and KDD techniques to reach data value. Big Data applications that are applied by big organizations will be discussed. Characteristics of big data will be introduced, which represent a significant challenge for Big Data management. Finally, some of the important future directions in Big Data field will be presented.

The ability to detect and track object of interest from sequence of frames is a critical and vital problem of many vision systems developed as yet. This paper presents a smart surveillance system that tracks objects of interest in a sequence of frames in their own defined respective boundaries. The objects of interest are registered or saved within the system. We have proposed a unique tracking algorithm using combination of SURF feature matching, Kalman filtering and template matching approach. Moreover, an efficient technique is proposed that is used to refine registered object image, extract object of interest and remove extraneous image area from it. The system will track registered objects in their respective boundaries using real time video generated through two IP cameras positioned in front of each other.

In recent years, the healthcare sector has shown inclination towards restructuring of healthcare systems to harmonize with technological innovations and adopting decision support system in routine clinical practices. The objective of this paper is to summarize challenges of Clinical Decision Support System (CDSS) and focus on the effectiveness of CDSS to improve clinical practice. This paper also describes the experience of CDSS in healthcare sector in Saudi Arabia and addresses the requirements for implementing successful CDSS with a real example. This study concludes that healthcare sector is in dire need to increase quality of patients' care and improve clinical practices by adopting CDSS.

Alignment overcomes divergence in the specification of the semantics of vocabularies by different but overlapping ontologies. Therefore, it enhances semantic interoperability for many web based applications. However, ontology change following applications new requirements or new perception of domain knowledges can leads to undesirable knowledge such as inconsistent and therefore to a useless alignment. Ontologies and alignments are encoded in knowledge bases allowing applications to store only some explicit knowledge while they derive implicit ones by applying reasoning services on these knowledge bases. This underlying representation of ontologies and alignments leads us to follow base revision theory to deal with alignment revision under ontology change. For that purpose, we adapt kernel contraction framework to design rational operators and to formulate the set of postulates that characterize each class of these operators. We demonstrate the connection between each class of operators and the set of postulates that characterize them. Finally, we present algorithms to compute alignment kernels and incision functions. Kernels are sets of correspondences responsible of undesirable knowledge following alignment semantics. Incision functions determine the sets of correspondences to eliminate in order to restore alignment consistency or to realize a successful contraction.

In the current industrial world, Time and cost are two the most important concepts affecting whole our planning, activities and scheduling. Effective use of these factors, will lead to increasing performance and profit. Solving the parallel-machine problem is one of the basic and important problems in industrial and service delivery systems. In this paper, a new mathematical multi-objective linear programming model is proposed for scheduling the parallel machines to minimize the total make-span and total machines cost. The proposed model is implemented in Matlab using the NSGA-II approach and the results are compared with MOPSO approach. The computational results show the effectiveness and superiority of the proposed model.

Recently, big data analysis has become an imperative task for many big companies. Map-Reduce, an emerging distributed computing paradigm, is known as a promising architecture for big data analytics on commodity hardware. Map-Reduce, and its open source implementation Hadoop, have been extensively accepted by several companies due to their salient features such as scalability, elasticity, fault-tolerance and flexibility to handle big data. However, these benefits entail a considerable performance sacrifice. The performance of a Map-Reduce application depends on various factors including the size of the input data set, cluster resource settings etc. A clear understanding of the factors that affect Map-Reduce application performance and the cost associated with those factors is required. In this paper, we study different performance parameters and an existing Cost Optimizer that computes the cost of Map-Reduce job execution. The cost based optimizer also considers various configuration parameters available in Hadoop that affect performance of these programs. This paper is an attempt to analyze the Map-Reduce application performance and identifying the key factors affecting the cost and performance of executing Map-Reduce applications.

Regression Testing is a performed to ensure modified code does not have any unintended side effect on the software. If regression testing is performed with retest-all method it will be very time consuming as testing activity. Therefore test suite reduction methods are used to reduce the size of original test suite. Objective of test suite reduction is to reduce those test cases which are redundant or less important in their fault revealing capability. Test suite reduction can only be used when time is critical to run all test cases and selective testing can only be done. Various methods exist in the literature related to test suite reduction of traditional software. Most of the methods are based of single objective optimization. In case of multi objective optimization of test suite, usually researchers assign different weight values to different objectives and combine them as single objective. However in test suite reduction multiple Pareto-optimal solutions are present, it is difficult to select one test case over other. Since GUI based software is our concern there exist very few reduction techniques and none of them consider multiple objective based reduction. In this work we propose a new test suite reduction technique based on two objectives, event weight and number of faults identified by test case. We evaluated our results for 2 different applications and we achieved 20% reduction in test suite size for both applications. In Terp Paint 3.0 application compromise 15.6% fault revealing capability and for Notepad 11.1% fault revealing capability is reduced.

In the world of internet, e-commerce is one of the most prominent sectors where user wants to shop and pay online for online products. E-cash is one of these payment methods. In e-cash, every time a unique string is generated for user so that user uses that string to pay for any online product. At the time of online purchasing a trust should be maintain between customers and merchant such that the product price which is going to pay by customer is fair or not, the merchant is indeed genuine to deliver the product after getting online payment or not. Trust issues are resolved by using fair exchange concept at the time of online purchasing. Anonymity is also a major concern; it means that true identity of users must be hidden from merchant. By keeping these issues in mind we proposed a protocol which ensures users anonymity by using e-cash payment method and fair exchange by using off-line TTP which invokes by customer when any dispute occur from merchant side. In this paper, we implement our proposed protocol and also analyze its performance and compare it with other protocol.

Linking and tracking news stories covering the same events written in different languages is a challenging task. In natural languages same information may be expressed in multiple ways and newspapers try to exploit this feature for making the news stories more appealing. It has been observed that the same news story is presented in same as well as in different language in different ways but normally the gist remains the same. Diversity of linguistic expressions presents a major challenge in identifying and tracking news stories covering the same events across languages, but doing so may provide rich and valuable resources as comparable and parallel corpora can be generated with this resource. In the case of Indian languages there exist limited language resources for Natural Language Processing and Information Retrieval tasks and identifying comparable and parallel documents would offer a potential source for deriving bilingual dictionaries and training statistical Machine Translation systems. Paraphrasing is the most common way of reproducing news stories and translated text is also a type of paraphrase. Prior to linking monolingual or bilingual news stories, these paraphrase types need to identified and classified to help researchers to devise techniques to solve these challenging problems. English-Hindi language pair not only differs in their scripts but also in their grammar and vocabulary. A number of paraphrase typologies have been built from the perspective of Natural Language Processing or for some or the other specific applications but as per the knowledge of the authors, no typology have been reported for English-Hindi cross language text reuse. In this paper a typology is formulated for cross lingual journalistic text reuse in English-Hindi. Typology unravels level of difficulties in English-Hindi mapping. It shall help in devising techniques for linking and tracking English-Hindi stories.

Vehicular Ad Hoc network is a versatile mobile wireless Ad-Hoc network targeted to support traffic monitoring, vehicular safety and many more applications. For the robust and reliable services in the VANET there is need to investigate the performance under frequent handovers in Mobile IP to prevent packet loss. Mobile IP is an interface that helps to track the mobile nodes and deliver messages even if vehicles are out of the coverage area of home node. In order to find the achievable performance bounds in terms of throughput, packet drop, collision rate and packet broadcast rate, extensive simulations have been done. A realistic city scenario has been proposed here by using the Rayleigh channel simulator, mobile IP enabled IEEE 802.11p OBUs and RSUs. The transmission powers of RSUs and threshold power levels have been varied to obtain the optimum performance through realistic conditions. Simulations are performed using NCTUns6.0 (National Chiao Tung University Network Simulator) in mobile IP interface.