We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

A privacy preserving association rule mining algorithm was introducedThis algorithm preserved privacy of individual values by computing scalar productMeanwhile the algorithm of computing scalar product was given and the security was analyzed

Privacy preserving association rule mining in vertically partitioned data

Process of information extraction (IE) is used to extract useful information from unstructured or semi-structured data. Big data arise new challenges for IE techniques with the rapid growth of multifaceted also called as multidimensional unstructured data. Traditional IE systems are inefficient to deal with this huge deluge of unstructured big data. The volume and variety of big data demand to improve the computational capabilities of these IE systems. It is necessary to understand the competency and limitations of the existing IE techniques related to data pre-processing, data extraction and transformation, and representations for huge volumes of multidimensional unstructured data. Numerous studies have been conducted on IE, addressing the challenges and issues for different data types such as text, image, audio and video. Very limited consolidated research work have been conducted to investigate the task-dependent and task-independent limitations of IE covering all data types in a single study. This research work address this limitation and present a systematic literature review of state-of-the-art techniques for a variety of big data, consolidating all data types. Recent challenges of IE are also identified and summarized. Potential solutions are proposed giving future research directions in big data IE. The research is significant in terms of recent trends and challenges related to big data analytics. The outcome of the research and recommendations will help to improve the big data analytics by making it more productive.

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An analytical study of information extraction from unstructured and multidimensional big data

In last few decades, with the advent of World Wide Web (WWW), world is being overloaded with huge data. This huge data carries potential information that once extracted, can be used for betterment of humanity. Information from this data can be extracted using manual and automatic analysis. Manual analysis is not scalable and efficient, whereas, the automatic analysis involves computing mechanisms that aid in automatic information extraction over huge amount of data. WWW has also affected overall growth in scientific literature that makes the process of literature review quite laborious, time consuming and cumbersome job for researchers. Hence a dire need is felt to automatically extract potential information out of immense set of scientific articles to automate the process of literature review. Therefore, in this study, aim is to present the overall progress concerning automatic information extraction from scientific articles. The information insights extracted from scientific articles are classified in two broad categories i.e. metadata and key-insights. As available benchmark datasets carry a significant role in overall development in this research domain, existing datasets against both categories are extensively reviewed. Later, research studies in literature that have applied various computational approaches applied on these datasets are consolidated. Major computational approaches in this regard include Rule-based approaches, Hidden Markov Models, Conditional Random Fields, Support Vector Machines, Naive-Bayes classification and Deep Learning approaches. Currently, there are multiple projects going on that are focused towards the dataset construction tailored to specific information needs from scientific articles. Hence, in this study, state-of-the-art regarding information extraction from scientific articles is covered. This study also consolidates evolving datasets as well as various toolkits and code-bases that can be used for information extraction from scientific articles.

Information extraction from scientific articles: a survey

Bayesian networks in healthcare: Distribution by medical condition

Cluster Based Data Aggregation Scheme for Latency and Packet Loss Reduction in WSN

Software test-case generation is the process by which test cases are generated based on the test conditions of the software that is generated. To solve these problem lots of research works have been done. The most commonly used methodologies are random test-case generation, symbolic test-case generation and dynamic test-case generation. The proposed technique uses genetic algorithm for automatic test-case generation in software testing. The Genetic Algorithm (GA) is an optimization heuristic technique that is implemented through natural evolution and fitness function. The genetic algorithm uses selection, crossover point and mutation operators to generate new test-cases from existing test sequence. The Tabu search lists long term and short term test cases. The proposed technique is a combination of genetic Algorithm and Tabu search and hence is called as a Hybrid Approach which helps in optimization of test cases. The short term list contains less frequently occurred test cases and long term list contains more frequently occurred test cases. The more frequently occurred test cases are used to test the software at first iteration. The generated long term test cases are more effective in finding faults in software program.

Test Case Optimization Using Hybrid Search Technique

Regression testing is testing the software in order to make sure that the modification made on the program lines does not affect the other parts of th e software, it is in maintenance phase and accounts for 80% of the maintenance cost and thus optimizing reg ression testing is one of the prime motives of software testers. Here we take the advantage of sel ecting test case information available in regressio n testing and prioritize them based on the number of modified lines covered by the test case, the test c ase which covers the most number of modified lines has the highest priority and is executed first and the one with the least coverage of modified lines has the l owest priority and is executed last provided deadli ne time is not reached, thus even if the testing is no t finished we will have covered maximum modified lines, the prioritization of the test cases are don e using the genetic algorithm, the genetic algorith m takes test case information from regression testing as in put and produces a sequence of test case to be exec uted such that the maximum number of modified code is covered.

Optimization of test cases by prioritization

Regression testing is one of the testing methods, which is done to assure that the changes made in fixes or other improvement changes does not affect the previously developed functionality of the software. Due to the reasons mentioned below, the regression testing posses a significant place in the software testing. Since it reduces the gaps of an application, modification has to be created and tested, test the new modifications to check that the modification did not change the any other part of the application, test coverage must be enhanced without conciliation of timelines. Thus, the test case generation and test case selection is the import role in the SUT (Software Under Test). More techniques are proposed to overcome these issues however those techniques could not be achieved complete code coverage with less time duration. In this paper proposed a hybrid algorithm which is used to resolve an optimal test case sequence selection as well as new test case generation for regression testing. The experimental results are shown our proposed approach is achieved better results rather than other approaches.

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Structural software testing: hybrid algorithm for optimal test sequence selection during regression testing

Information Extraction, which is an area of natural language processing that deals with finding factual information in free text. The task of Information Extraction (IE) is to identify a predefined set of concepts i.e., a representation of the information that is machine understandable. The classic IE tasks include Named Entity Recognition (NER) addresses the problem of the identification and classification of predefined types of named entities. Co-reference Resolution (CO), Relation Extraction (RE),Event Extraction (EE) Named Entity Recognition (NER) aims to extract and to classify rigid designators in text such as proper names, biological species, and temporal expressions. In this paper, we present various efficient NER techniques and modeling's . Named entity recognition addresses the problem of locating textual mentions of predefined types of entities, where the entity categories can be very diverse, ranging from people and companies in business applications to cells and proteins in biomedical applications.

T. Ravi

Papers

Cluster Based Data Aggregation Scheme for Latency and Packet Loss Reduction in WSN

Test Case Optimization Using Hybrid Search Technique

Optimization of test cases by prioritization

Structural software testing: hybrid algorithm for optimal test sequence selection during regression testing

Modelings and techniques in named entity recognition: an information extraction task