The latest disruptive trends and developments in digital age comprise social networking, mobility, analytics and cloud, popularly known as SMAC. The year 2016 saw Big Data Technologies being leveraged to power business intelligence applications. What holds in store for 2020 and beyond?

Big Data Analytics

The Combinatorial problems are real world decision making problem with discrete and disjunctive choices. When these decision making problems involve more than one conflicting objective and constraint, it turns the polynomial time problem into NP-hard. Thus, the straight forward approaches to solve multi-objective problems would not give an optimal solution. In such case evolutionary based meta-heuristic approaches are found suitable. In this paper, a novel particle swarm optimization based meta-heuristic algorithm is presented to solve multi-objective combinatorial optimization problems. Here a mapping method is considered to convert the binary and discrete values (solution encoded as particles) to a continuous domain and update it using the velocity and position update equation of particle swarm optimization to find new set of solutions in continuous domain and demap it to discrete values. The performance of the algorithm is compared with other evolutionary strategy like SPEA and NSGA-II on pseudo-Boolean discrete problems and multi-objective 0/1 knapsack problem. The experimental results confirmed the better performance of combinatorial particle swarm optimization algorithm.

A Novel Particle Swarm Optimization Algorithm for Multi-Objective Combinatorial Optimization Problem

The exponential growth of available information and the deployment of knowledge management systems delivers excessive information to the end users that they cannot manage at once. This problem has led to an increased emphasis on solutions to information overload. Searching and browsing are two methods to locate information. Many studies have focused on solving the information overload problem in the searching process, but the methods to alleviate information overload in browsing process have not been adequately studied. Hence, a method that addresses information overload in the browsing process is presented in this paper. The aim is to reduce the information overload during browsing domain knowledge for new knowledge users who have little understanding of the information. In this method, a knowledge map and social network analysis are utilized to navigate the knowledge users. Technologies first construct a knowledge map from text mining and the important knowledge that includes more information about the domain is then identified via social network analysis. Based on this process, the knowledge user can browse the domain knowledge starting from the important knowledge and navigate via the knowledge map. We applied the method to assist new knowledge users in browsing the Computer Numerical Control (CNC) domain knowledge base to validate the method. The results indicate that the method can identify the important knowledge at a highly acceptable level, the constructed knowledge map can efficiently navigate the knowledge users, and the information overload can be significantly decreased.

Knowledge map-based method for domain knowledge browsing

The purpose of this study is to underpin the impact of cloud computing in global accounting information systems (AIS) Moreover, it investigates the key aspects that ought to be considered by the organization before choosing to pick the correct accounting system,The study looks at and depends on narrative investigation of previous studies In the examination talked about the principle issues with respect to the utilization of cloud and database the executives in the AIS through developing research model,The focal point of the paper is the impact of cloud computing worldview on the business area This paper highlights different facts of cloud accounting, published research papers and the benefits and possible risks determined by the implementation of cloud services, mostly in relation to the accounting department,Considering the numerous ramifications of cloud advancements on the present business process, there is a need for an examination of how these innovations will be used in AIS to improve precision, benefits and risks At the same time, there is need to investigate the determinant elements of actualizing cloud advances in the AIS More importantly, to the author’s knowledge, this is the first study that focuses on the number of published research works to show the importance of cloud computing in accounting and information systems

Role of cloud computing in global accounting information systems

Several users use metasearch engines directly or indirectly to access and gather data from more than one data sources. The effectiveness of a metasearch engine is majorly determined by the quality of the results and it returns and in response to user queries. The rank aggregation methods which have been proposed until now exploits very limited set of parameters such as total number of used resources and the rankings they achieved from each individual resource. In this work, we use the neural network to merge the score computation module effectively. Initially, we give a query to different search engines and the top n list from each search engine is chosen for further processing our technique. We then merge the top n list based on unique links and we do some parameter calculations such as title based calculation, snippet based calculation, content based calculation, domain calculation, position calculation and co-occurrence calculation. We give the solutions of the calculations with user given ranking of links to the neural network to train the system. The system then rank and merge the links we obtain from different search engines for the query we give. Experimentation results reports a retrieval effectiveness of about 80%, precision of about 79% for user queries and about 72% for benchmark queries. The proposed technique also includes a response time of about 76 ms for 50 links and 144 ms for 100 links.

Artificial neural network-based merging score for Meta search engine

Search computing focuses on building answers to complex search queries (for example, "Where can I attend an interesting conference in my field near a sunny beach?") by interacting with a constellation of cooperating search services, and using result ranking and joining as the dominant factors for service composition. The service computing paradigm has so far been neutral to the specific features of search applications and services. To address this weakness, search computing advocates a new approach in which search, join, and ranking are the central aspects for service composition.

/pdf/search-computing-managing-complex-search-queries-3p13qjfs92.pdf

Search Computing: Managing Complex Search Queries

The use of patterns in data management is not new: in data warehousing, data marts are simple conceptual schemas with exactly one core entity, describing facts, surrounded by multiple entities, describing data analysis dimensions; data marts support special analysis operations, such as roll up, drill down, and cube. Similarly, Service Marts are simple schemas which match "Web objects" by hiding the underlying data source structures and presenting a simple interface, consisting of input, output, and rank attributes; attributes may have multiple values and be clustered within repeating groups. Service Marts support Search Computing operations, such as ranked access and service compositions. When objects are accessed through Service Marts, responses are ranked lists of objects, which are presented subdivided in chunks, so as to avoid receiving too many irrelevant objects - cutting results and showing only the best ones is typical of search services. This paper gives a formal definition of Service Marts and shows how Service Marts can be implemented and used for building Search Computing applications.

/pdf/designing-service-marts-for-engineering-search-computing-4n89k4kobg.pdf

Designing service marts for engineering search computing applications

In this paper we propose a query disambiguation mechanism focalizing the query context by applying clustering to the results of a Web-search. The clusters are ranked to reflect a balance of their contents’ novelty and overall similarity with respect to the original query, and, from each of them, a disambiguated query is generated so as to potentially retrieve new documents focalized on the cluster contents.

Query Disambiguation Based on Novelty and Similarity User's Feedback

The use of patterns in data management is not new: in data warehousing, data marts are simple conceptual schemas with exactly one core entity, describing facts, surrounded by multiple entities, describing data analysis dimensions; data marts support special analysis operations, such as roll up, drill down, and cube. Similarly, service marts are simple schemas which match "Web objects" by hiding the underlying data source structures and presenting a simple interface, consisting of input, output, and rank attributes; attributes may have multiple values and be clustered within repeating group. Service marts support Search Computing operations, such as ranked access and joins. When objects are accessed through service marts, responses are ranked lists of objects, which are presented subdivided in chunks, so as to avoid receiving too many irrelevant objects – cutting results and showing only the best ones is typical of search services. This chapter includes a survey of service definition standards (discussing the standards for service description and the current state-of-the-art for service registration and discovery), then introduces a formal definition of service marts and of connection patterns at the conceptual, logical, and physical levels. Then, we show how service marts can be implemented, by taking into account different kinds of data sources, and taking advantage of components (written in Java and SQL) and tools (such as a materialize specifically developed to help service mart implementation). We use such components and tools to build a collection of services used in a running example throughout the chapters of this part.

Chapter 9: service marts

A flexible language for exploring clustered search results

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