Sketch of Big Data Real-Time Analytics Model

Big Data has drawn huge attention from researchers in information sciences, decision makers in governments and enterprises. However, there is a lot of potential and highly useful value hidden in the huge volume of data. Data is the new oil, but unlike oil data can be refined further to create even more value. Therefore, a new scientific paradigm is born as data-intensive scientific discovery, also known as Big Data. The growth volume of real-time data requires new techniques and technologies to discover insight value. In this paper we introduce the Big Data real-time analytics model as a new technique. We discuss and compare several Big Data technologies for real-time processing along with various challenges and issues in adapting Big Data. Real-time Big Data analysis based on cloud computing approach is our future research direction

Big Data Mining and Semantic Technologies: Challenges and Opportunities

Big data a term coined due to the explosion in the quantity and diversity of high frequency digital data which is having a potential for valuable insights has drawn the most attention in the area of research and development. Converting big data to actionable insights requires depth understanding of big data, its characteristics, challenges and current technological trends. A rise of big data is changing the existing data storage, management, processing and analytical mechanisms and leads to the new architecture/ecosystems to handle big data applications. This paper covers finding of our research study about big data characteristic, various types of analysis associated with it and basic big data types. First, we are presenting the big data study from data mining and analysis perspective and discuss the challenges and next, we present the result of research study on meaningful use of big data in the context of semantic technologies. Moreover, we discuss various case studies related to social media analysis and recent development trends to identify potential research directions for big data with semantic technologies. DOI: 10.17762/ijritcc2321-8169.150711

Evolution to Big Data Analytics Techniques and Challenging Issues in Data Mining With Big Data

Big Data is another term used to recognize the datasets that because of their enormous size and multifaceted nature. Big Data are currently quickly growing in all science and engineering domains, including physical, natural and biomedical sciences. Big Data mining is the capacity of separating helpful information from these huge datasets or floods of data, that because of its volume, changeability, and velocity, it was impractical before to do it. The Big Data challenge is getting one of the most energizing open doors for the following years. In the present time of digitization, we take a shot at the variety of data. Colossal measure of data will be prepared by Google, Microsoft and Amazon. Regular routine these organization prepared huge measure of data. In such way we have to require some approach to adjust the innovation in with the end goal that every one of the data will be prepared adequately. Big Data is a developing concept that depicts imaginative systems and innovations to break down enormous volume of complex datasets that are exponentially produced from different sources and with different rates. Data mining procedures are giving extraordinary guide in the region of Big Data examination, since managing Big Data are big difficulties for the applications. Big Data examination is the capacity of removing valuable information from such colossal datasets. This paper exhibits a writing survey that incorporate the significance, difficulties and applications of Big Data in different fields and the various methodologies utilized for Big Data Analysis utilizing Data Mining procedures. The discoveries of this audit give important information to the analysts about the primary patterns in research and examination of Big Data utilizing diverse investigation domains. This examination paper incorporates the information about what is big data, Data mining, Data mining with big data, Challenging issues and its related work

Action Recognition in Videos: from Motion Capture Labs to the Web

This paper presents a survey of human action recognition approaches based on visual data recorded from a single video camera. We propose an organizing framework which puts in evidence the evolution of the area, with techniques moving from heavily constrained motion capture scenarios towards more challenging, realistic, "in the wild" videos. The proposed organization is based on the representation used as input for the recognition task, emphasizing the hypothesis assumed and thus, the constraints imposed on the type of video that each technique is able to address. Expliciting the hypothesis and constraints makes the framework particularly useful to select a method, given an application. Another advantage of the proposed organization is that it allows categorizing newest approaches seamlessly with traditional ones, while providing an insightful perspective of the evolution of the action recognition task up to now. That perspective is the basis for the discussion in the end of the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4 table

A Survey on Big data Analytics in Cloud Environment

The continuous and rapid growth in the volume of data captured by organizations, such as social media, Internet of Things (IoT), machines, multimedia, GPS has produced an overwhelming flow of data. Data creation is occurring at a record rate, referred to as big data, and has emerged as a widely recognized trend. To take advantage of big data, real-time analysis and reporting must be provided in tandem with the massive capacity needed to store and process the data. Big data is affecting organization such as Banking, Education, Government, Health care, Manufacturing, retails and eventually, the society. On the other hand, Cloud computing eliminates the need to maintain expensive computing hardware, dedicated space, and software. Cloud provides larger volume of space for the storage and different set of services for all kind of applications to the cloud customers. Therefore, all the companies are nowadays migrating their applications towards cloud environment, because of the huge reduce in the overall investment and greater flexibility provided by the cloud

Critical analysis of Big Data Challenges and analytical methods

Big Data (BD), with their potential to ascertain valued insights for enhanced decision-making process, have recently attracted substantial interest from both academics and practitioners. Big Data Analytics (BDA) is increasingly becoming a trending practice that many organizations are adopting with the purpose of constructing valuable information from BD. The analytics process, including the deployment and use of BDA tools, is seen by organizations as a tool to improve operational efficiency though it has strategic potential, drive new revenue streams and gain competitive advantages over business rivals. However, there are different types of analytic applications to consider. Therefore, prior to hasty use and buying costly BD tools, there is a need for organizations to first understand the BDA landscape. Given the significant nature of the BD and BDA, this paper presents a state-of-the-art review that presents a holistic view of the BD challenges and BDA methods theorized/proposed/employed by organizations to help others understand this landscape with the objective of making robust investment decisions. In doing so, systematically analysing and synthesizing the extant research published on BD and BDA area. More specifically, the authors seek to answer the following two principal questions: Q1 – What are the different types of BD challenges theorized/proposed/confronted by organizations? and Q2 – What are the different types of BDA methods theorized/proposed/employed to overcome BD challenges?. This systematic literature review (SLR) is carried out through observing and understanding the past trends and extant patterns/themes in the BDA research area, evaluating contributions, summarizing knowledge, thereby identifying limitations, implications and potential further research avenues to support the academic community in exploring research themes/patterns. Thus, to trace the implementation of BD strategies, a profiling method is employed to analyze articles (published in English-speaking peer-reviewed journals between 1996 and 2015) extracted from the Scopus database. The analysis presented in this paper has identified relevant BD research studies that have contributed both conceptually and empirically to the expansion and accrual of intellectual wealth to the BDA in technology and organizational resource management discipline

Large spatial datasets: Present Challenges, future opportunities

The key advantages of a well-designed multidimensional database is its ability to allow as many users as possible across an organisation to simultaneously gain access and view of the same data. Large spatial datasets evolve from scientific activities (from recent days) that tends to generate large databases which always come in a scale nearing terabyte of data size and in most cases are multidimensional. In this paper, we look at the issues pertaining to large spatial datasets; its feature (for example views), architecture, access methods and most importantly design technologies. We also looked at some ways of possibly improving the performance of some of the existing algorithms for managing large spatial datasets. The study reveals that the major challenges militating against effective management of large spatial datasets is storage utilization and computational complexity (both of which are characterised by the size of spatial big data which now tends to exceeds the capacity of commonly used spatial computing systems owing to their volume, variety and velocity). These problems fortunately can be combated by employing functional programming method or parallelization techniques

Adaptive Approach of Data Mining Using HACE Algorithm

Data mining is an interdisciplinary subfield of computer science, is the computational process of discover patterns in large data sets involving methods at the intersection of artificial intelligence , machine learning, statistic, and database systems. Big Data is a new term used to identify the datasets that due to their large size and complication. Data comes from everywhere, sensors used to gather climate information, post to social media sites, digital pictures and videos etc. this data is known as big data. Big Data concern large-volume, difficult, growing data sets with many, independent sources. With the fast development of networking, data storage, and the data group ability, Big Data is now fast expanding in all science and work domains, including physical, biological and bio-medical science. This paper gives brief idea about a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining point of view. DOI: 10.17762/ijritcc2321-8169.15038