Big Data computing and clouds

Social big data

The age of big data is now coming. But the traditional data 
analytics may not be able to handle such large quantities of data. The question that arises now is, how to develop a high performance platform to efficiently analyze big data and 
how to design an appropriate mining algorithm to find the useful things from big data. To deeply discuss this issue, this paper begins with a brief introduction to data analytics, followed by the discussions of big data analytics. 
Some important open issues and further research directions will also be presented for the next step of big data analytics.

/pdf/big-data-analytics-a-survey-2m5v4g89fb.pdf

Big data analytics: a survey

Description logics and reasoners, which are descendants of the KL-ONE language, have been studied in depth in artificial intelligence. After a brief introduction, we survey their application to the problems of information management, using the framework of an abstract information server equipped with several operations-each involving one or more languages. Specifically, we indicate how one can achieve enhanced access to data and knowledge by using descriptions in languages for schema design and integration, queries, answers, updates, rules, and constraints. >

Description logics in data management

. In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets. Furthermore, decision makers need to be able to gain valuable insights from such varied and rapidly changing data, ranging from daily transactions to customer interactions and social network da-ta. Such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper aims to analyze some of the different analytics methods and tools which can be applied to big data, as well as the opportunities provided by the application of big data analytics in various decision domains. Keywords: big data, data mining, analytics, decision making.

Big Data Analytics: A Literature Review Paper

In this paper, we provide an overview of state-of-the-art research issues and achievements in the field of analytics over big data, and we extend the discussion to analytics over big multidimensional data as well, by highlighting open problems and actual research trends. Our analytical contribution is finally completed by several novel research directions arising in this field, which plays a leading role in next-generation Data Warehousing and OLAP research.

/pdf/analytics-over-large-scale-multidimensional-data-the-big-3xxthheu45.pdf

Analytics over large-scale multidimensional data: the big data revolution!

The popularity of data warehouses for analysis of data has grown tremendously, but much of the creation of data warehouses is done manually. We propose and illustrate algorithms for automatic conceptual schema development and evaluation. Our creation algorithm uses an enterprise schema of an operational database as a starting point for source-driven data warehouse schema design. Candidate conceptual schemas are created using the ME/R model, extended to note where additional user input can be used to further refine a schema. Our evaluation algorithm follows a user-driven requirements approach that utilizes queries to guide selection of candidate schemas most likely to meet user needs. In addition, we propose a guideline of manual steps to refine a conceptual schema to suit additional user needs, for example, the level of detail needed for date fields. The algorithms are illustrated using the TPC-H Benchmark schema and queries. Our algorithms provide a foundation for a software tool to create and evaluate data warehouse conceptual schemas.

/pdf/automating-data-warehouse-conceptual-schema-design-and-3h1clwba53.pdf

Automating data warehouse conceptual schema design and evaluation.

This part of the review aims to reduce the start-up burden of data collection and descriptive analytics for statistical modeling and route optimization of risk associated with motor vehicles. From a data-driven bibliometric analysis, we show that the literature is divided into two disparate research streams: (a) predictive or explanatory models that attempt to understand and quantify crash risk based on different driving conditions, and (b) optimization techniques that focus on minimizing crash risk through route/path-selection and rest-break scheduling. Translation of research outcomes between these two streams is limited. To overcome this issue, we present publicly available high-quality data sources (different study designs, outcome variables, and predictor variables) and descriptive analytic techniques (data summarization, visualization, and dimension reduction) that can be used to achieve safer-routing and provide code to facilitate data collection/exploration by practitioners/researchers. Then, we review the statistical and machine learning models used for crash risk modeling. We show that (near) real-time crash risk is rarely considered, which might explain why the optimization models (reviewed in Part 2) have not capitalized on the research outcomes from the first stream.

A Review of Data Analytic Applications in Road Traffic Safety. Part 1: Descriptive and Predictive Modeling.

In this paper, we describe our experience running "Girls on the Go: The Mobile Computing College Experience." We decided to do a residential summer camp for HS-age girls to achieve two goals: to encourage our campers to attend college and to interest them in computer science as a possible career option. We centered the camp around the design of a zoo-themed research tool where campers designed a mobile application to be used by researchers to document and study animal behavior. Post-camp surveys gave statistically significant results indicating that the camp increased the girls' confidence in performing computer science and understanding what computer scientists did.

Girls on the go: a CS summer camp to attract and inspire female high school students

We apply multiple query optimization to batches of pull (on-demand) requests in a mobile database system. The resulting view can answer several queries at once, and it is broadcast on a view channel dedicated to common answers of multiple queries rather than over individual downlink channels. A performance study is conducted that simulates different query workloads. The results indicate a significant savings in channel bandwidth usage and a reduction in average wait time for a multi-query approach compared to a traditional pull-based approach.

/pdf/applying-multiple-query-optimization-in-mobile-databases-53oc9p5j5c.pdf

Karen C. Davis

Papers

Analytics over large-scale multidimensional data: the big data revolution!

Automating data warehouse conceptual schema design and evaluation.

A Review of Data Analytic Applications in Road Traffic Safety. Part 1: Descriptive and Predictive Modeling.

Girls on the go: a CS summer camp to attract and inspire female high school students

Applying multiple query optimization in mobile databases