Real-time big data processing for anomaly detection: A Survey

Big data has become a significant research area due to the birth of enormous data generated from various sources like social media, internet of things and multimedia applications. Big data has played critical role in many decision makings and forecasting domains such as recommendation systems, business analysis, healthcare, web display advertising, clinicians, transportation, fraud detection and tourism marketing. The rapid development of various big data tools such as Hadoop, Storm, Spark, Flink, Kafka and Pig in research and industrial communities has allowed the huge number of data to be distributed, communicated and processed. Big data applications use big data analytics techniques to efficiently analyze large amounts of data. However, choosing the suitable big data tools based on batch and stream data processing and analytics techniques for development a big data system are difficult due to the challenges in processing and applying big data. Practitioners and researchers who are developing big data systems have inadequate information about the current technology and requirement concerning the big data platform. Hence, the strengths and weaknesses of big data technologies and effective solutions for Big Data challenges are needed to be discussed. Hence, due to that, this paper presents a review of the literature that analyzes the use of big data tools and big data analytics techniques in areas like health and medical care, social networking and internet, government and public sector, natural resource management, economic and business sector. The goals of this paper are to (1) understand the trend of big data-related research and current frames of big data technologies; (2) identify trends in the use or research of big data tools based on batch and stream processing and big data analytics techniques; (3) assist and provide new researchers and practitioners to place new research activity in this domain appropriately. The findings of this study will provide insights and knowledge on the existing big data platforms and their application domains, the advantages and disadvantages of big data tools, big data analytics techniques and their use, and new research opportunities in future development of big data systems.

The state of the art and taxonomy of big data analytics: view from new big data framework

Sentiment detection and classification is the latest fad for social analytics on Web. With the array of practical applications in healthcare, finance, media, consumer markets, and government, distilling the voice of public to gain insight to target information and reviews is non‐trivial. With a marked increase in the size, subjectivity, and diversity of social web‐data, the vagueness, uncertainty and imprecision within the information has increased manifold. Soft computing techniques have been used to handle this fuzziness in practical applications. This work is a study to understand the feasibility, scope and relevance of this alliance of using Soft computing techniques for sentiment analysis on Twitter. We present a systematic literature review to collate, explore, understand and analyze the efforts and trends in a well‐structured manner to identify research gaps defining the future prospects of this coupling. The contribution of this paper is significant because firstly the primary focus is to study and evaluate the use of soft computing techniques for sentiment analysis on Twitter and secondly as compared to the previous reviews we adopt a systematic approach to identify, gather empirical evidence, interpret results, critically analyze, and integrate the findings of all relevant high‐quality studies to address specific research questions pertaining to the defined research domain.

Systematic literature review of sentiment analysis on Twitter using soft computing techniques

Heart disease identification from patients’ social posts, machine learning solution on Spark

Investment casting, also called lost wax casting, is widely used for producing ferrous and nonferrous metal parts. Unlike other casting processes, investment casting produces net shape parts with excellent surface finish and dimensional accuracy. This manufacturing process is ideal for applications that have relatively low production quantities (10 to 10,000 pieces) or rapidly changing product designs.

On the Investment Casting

Applying spark based machine learning model on streaming big data for health status prediction

Big Data from innumerable sources offers immense prospects for intelligent decision making in every single field, in spite of its challenging factors like huge volume, extreme variety or varying velocity. Big Data analytics can provide new perceptions from data, which would have otherwise unnoticed, while proper visualization of the data itself may result in solutions to Big Data problems through visual means by presenting the vital elements in the vast data in a human-interaction friendly format. But most of the traditional graphics and charting tools fail to draw the big picture from the big data that spans millions of records. This paper exhibits the effectiveness of visualization in exposing and communicating the essential facts in a large dataset using the two currently popular but divergent visualization tools, D3.js and Tableau, and performs a comparative analysis of the two.

Interactive visual analytics on Big Data: Tableau vs D3.js

Evolutions of metallic dental restorative materials are facilitated due to need in improvement in its quality such as mach in ability and aesthetics. Availability of numerous substitutes and advancement in the manufacturing technology is one of many

A review on metallic dental materials and its fabrication techniques

Background: Oral infection, especially periodontitis, may affect the course and pathogenesis of a number of systemic diseases. Present study was aimed to evaluate the potential association between periodontal health and respiratory infections. Materials and Methods: A cross-sectional, study was conducted among 100 adult patients (20–60 years of age) in which 50 patients belonged to group A (test group) and 50 belonged to group B (control group). Gingival Index (GI) was used to assess the gingival status and Periodontal indices such as Russell's Periodontal Index (PI) and Periodontal Index for Risk of Infectiousness (PIRI), which were assessed in all 100 patients. Results: The scores of GI, PI and PIRI were highly significant in COPD group than in non-COPD group. Conclusion: There is positive association between respiratory infections and periodontal diseases. Periodontal status may serve as a useful risk marker to identify persons at higher risk for COPD. Further longitudinal studies are required to establish a positive correlation between periodontal health and development of COPD.

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Siddhanth D. Shetty

Papers

Applying spark based machine learning model on streaming big data for health status prediction

Interactive visual analytics on Big Data: Tableau vs D3.js

A review on metallic dental materials and its fabrication techniques

Correlation between respiratory health and periodontal diseases in chhatisgarh population- a prospective cross-sectional study