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Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

Big Data has become a new source of opportunity among applications in Artificial Intelligence. Many design considerations exist in this relatively new field where parallel processing frameworks can be employed in a more economical fashion. Unlike traditional data sources, Big Data applications present their own unique challenges in order to appropriately harness the utility of open source frameworks including Apache Spark and design patterns predicated on the Directed Acyclic Graph. By embracing this new paradigm, parallel processing can be effectively leveraged to support development at a level of scale and performance that was not possible earlier.

Semantic Web standards were developed in order to improve accessibility to the web. While developers frequently leverage software APIs to support application development, considerable challenges still exist. The Semantic Web has promised to resolve these challenges, but to date has only provided limited functionality. Higher potential has been demonstrated in the area known as Linked Data which has leveraged Semantic Web technologies to support the purpose of building applications by linking data sets within a common model. This paper examines a number of potential applications for mobility which leverage Linked Data thereby providing direct access to digital libraries and information resources.

Big Data has become ubiquitous across all areas of research allowing for new applications that were not possible earlier. Unlike software development relying on traditional data sources, Big Data applications present their own unique challenges to appropriately harness the utility of the Apache Hadoop architecture. In this paper, we introduce fundamental concepts of Hadoop and explore its usage as well as future direction. We also present our strategy for exploring the Hadoop architecture including addressing issues of scalability, customization of code and utilization of programming techniques.

The ever-increasing amount of information flowing through Social Media presents numerous opportunities for the generation of Business Intelligence. Challenges exist in the leveraging of these data sources due to their heterogeneity and unstructured content. This paper presents the application of Semantic Computing to Social Media for industrial application, focusing on topic identification and behavior prediction. The methodologies described can benefit many areas of an organization including support of marketing, customer service, engineering and public relations. Results demonstrate that business operations can be substantially enhanced through application of Semantic Computing to Social Media.

With rapidly expanding data collections becoming increasingly available, the application of Semantic Computing has become imperative to leverage this resource for industrial applications. This paper presents a survey of Semantic Computing in the area of Business Intelligence. We examine semantic analytical techniques and tools as applied for prediction analysis and decision support. We also define the role of Semantic Computing as applied in the context of Data Mining, Text Mining and Big Data Analytics. Additionally, we describe how business data is queried with Structured Natural Language as well as the use of On-Line Analytic Processing.

One of the most substantial opportunities in Big Data is to integrate disparate data sources across the enterprise. To realize this goal it can be valuable to leverage environments developed for high speed parallel processing as well as toolkits supporting the development and maintenance of semantic information. In support of this approach a proposed framework and methodology is presented to utilize an ontology-based data integration strategy. Our approach supports a rule-based translation to generate new ontology versions within a fast prototyping environment leveraging the Jena API within the context of the Apache Spark environment.

In order to realize the promise of Big Data, applications will have to consider the integration of many disparate data sources. Due to data heterogeneity, this task presents a number of challenges that may not be completely resolved with existing Extract-Transform-Load (ETL)-based frameworks. In this paper, we explore the potential of Semantic Web Technologies as a means of integration and development of Big Data applications. In demonstration, a usage case study is presented examining supplier chain operations. Additionally, we review the overall challenges of data integration in this area.

A challenge in the research of Social Networks is the large scale analysis of graphs. One of the most valuable metrics in the evaluation of graphs is betweenness-centrality. In this paper, we define an approximation of betweenness-centrality for the purpose of building a predictive model of Social Networks. The methodology presented describes a bounded distance approximation of betweenness-centrality designed for implementation within a parallel architecture. Through our proposed design pattern, we are able to leverage Big Data technologies to determine metrics in the context of ever expanding internet-based data resources.

Due to its predictive nature, Social Media has proved to be an important resource in support of the identification of trends. In Customer Relationship Management there is a need beyond trend identification which includes understanding the topics propagated through Social Networks. In this paper, we explore topic modeling by considering the techniques of Latent Dirichlet Allocation which is a generative probabilistic model for a collection of discrete data. We evaluate this technique from the perspective of classification as well as identification of noteworthy topics as it is applied to a filtered collection of Twitter messages. Experiments show that these methods are effective for the identification of sub-topics as well as to support classification within large-scale corpora.

Big Data has become a new source of opportunity in terms of Semantic Computing. Unlike traditional data sources, Big Data applications present their own unique challenges in order to appropriately harness the utility of open source frameworks including Apache Hadoop and design patterns such as Map - Reduce. Through these advances, Big Data has become ubiquitous across all areas of research allowing for new applications that were not possible earlier. The Second IEEE International Workshop on Semantic Computing with Big Data (ICSC-SCBD'15) provides a forum for researchers to present their latest research progress, exchange ideas and thoughts, and explore new research directions. It continues seeking high quality research contributions and encouraging significant work that addresses the major challenges in the semantics as applied in the context of Big Data.

Twitter-based messages have presented challenges in the identification of features as applied to classification. This paper explores filtering techniques for improved trend detection and information extraction. Starting with a pre-filtered source (Twitter), we will examine the application of both information theory and Natural Language Processing (NLP) based techniques as a means of preprocessing for classification. Results demonstrate that both means allow for improved results in classification among highly idiosyncratic data (Twitter).

The MapReduce paradigm has become ubiquitous within Big Data Analytics. Within this field, Social Networks exist as an important area of applications as it relies on the large scale analysis of graphs. To enable the scalability of Social Networks, we consider the application of MapReduce design patterns for the determination of graph-based metrics. Specifically, we detail the application of a MapReduce-based solution for the metric of betweenness-centrality. The prevailing concept is separation of the graph topology from the actual graph analysis. Here, we consider the chaining of MapReduce jobs for the estimation of shortest paths in a graph as well as post processing statistics. Through our design pattern, we are able to leverage Big Data Technologies to determine metrics in the context of ever expanding internet-based data resources.