Rajeev Agrawal

Challenges of data integration and interoperability in big data

The enormous volumes of data created and maintained by industries, research institutions are on the verge of outgrowing its infrastructure. The advancements in the organizations work flow include data storage, data management, data maintenance, data integration, and data interoperability. Among these levels, data integration and data interoperability can be the two major focus areas for the organizations which tend to implement advancements in their workflow. Overall, data integration and data interoperability influence the organizations performance. The data integration and data interoperability are complex challenges for the organizations deploying big data architectures due to the heterogeneous nature of data used by them. Therefore, it requires a comprehensive approach to negotiate the challenges in integration and interoperability. This paper focuses on the challenges of data integration and data interoperability in big data.

A layer based architecture for provenance in big data

Big data is a new technology wave that makes the world awash in data. Various organizations accumulate data that are difficult to exploit. Government databases, social media, healthcare databases etc. are the examples of that big data. Big data covers absorbing and analyzing huge amount of data that may have originated or processed outside of the organization. Data provenance can be defined as origin and process of data. It carries significant information of a system. It can be useful for debugging, auditing, measuring performance and trust in data. Data provenance in big data is relatively unexplored topic. It is necessary to appropriately track the creation and collection process of the data to provide context and reproducibility. This poster tries to address the challenges of capturing provenance data. Additionally, we propose an intuitive layer based architecture of provenance in big data that can handle the challenges.

Trust in cloud computing

There has been a lot of research on users data security from technical aspects, however, there is not much work done to understand the psychology of consumers trust in the new Internet marketplace. This paper examines the issues surrounding the difficulty of the average Internet user to trust cloud service providers with the security of their data. By examining user sentiment we attempt to outline the scope of the problem and suggest how cloud service providers may overcome trust issues. We explore the issues of consumer trust in cloud computing by conducting a survey to establish consumer sentiment on trust issues in cloud computing. Finally, we analyze the survey results in a way that will be useful for cloud service providers to assess their approach towards customers and make changes.

Cloud forensics challenges from a service model standpoint: IaaS, PaaS and SaaS

Cloud computing is a promising and expanding technology which could replace traditional IT systems. Cloud computing resembles a giant pool of resources which contains hardware, software and related applications, which can be accessed through web-based services on a pay-per-usage model. The main features of the cloud model are accessibility, availability and scalability, and it can be subdivided into three service models: Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS). Cloud computing continues to transform how security challenges are addressed in closed and private networks. Given the advanced functionality offered by cloud computing, network monitoring and digital forensics efforts are potentially detectable and service-interruptive, which significantly impacts the effectiveness and thoroughness of digital forensic methods. This paper presents a general view of cloud computing, which aims to highlight the security issues and vulnerabilities associated with cloud service models. The technology is mainly based on virtualization, where data is always volatile and typically stored in a de-centralized architecture located across various countries and regions. This presents forensics investigators with legal challenges, due to the nature of multi-tenancy and distributed shared resources. This paper examines the three cloud service models and discusses the security challenges and issues involved with each service model along with potential solutions for each.

Scenario-based design for a cloud forensics portal

Cloud computing continues to transform how we address security challenges in closed and private networks. Given the advanced functionality offered by cloud computing, network monitoring and digital forensics efforts are potentially detectable and service interruptive which affects the effectiveness and thoroughness of digital forensic methods. Virtualization, the cost-effective delivery platform for clouds and data centers, provides an opportunity for Virtual Machine Introspection (VMI) through the hypervisor. VMI would be an environment to monitor the activity of a Virtual Machine (VM). In this paper, we propose a multi-phase scenario-based design concept within an open source virtualized environment to collect data to validate the hypervisors ability to provide a portal for threat monitoring that is effective, undetectable, and non-interruptive.

Assessment of ARIMA-based prediction techniques for road-traffic volume

Studies related to public transportation systems help the commuting public by increasing road safety and circulation. These result in optimized traffic flow, shorter origin-destination travel time and reduced incident rate. Vehicular Ad-Hoc Network uses a number of sensors to gather data on the road. Intelligent Transportation Systems (ITS) draw inference from the gathered data. In this paper we discuss our experience of using Auto Regressive Integrated Moving Average (ARIMA) based techniques emphasizing on the integration of short-range and long-range dependencies of the historical traffic volume. We also analyze traffic data for patterns across different types of roads and derive computational complexity of ARIMA. Finally, improvements are identified for better prediction. We empirically show that SARIMA and ARIMA-GARCH exhibit similar road traffic prediction. ARIMA-GARCH is better than ARIMA and SARIMA for prediction, with stable model order across different historical traffic volumes. We further analyzes model orders across different types of roads and historical traffic volume; and its implications for practical applicability in ITS.

Mind the Gaps-Finding the Appropriate Dimensional Representation for Semantic Retrieval of Multimedia Assets

Multimedia retrieval is an enabling technology for the new semantic web, since discussing multimedia in the framework of the semantic web can be placed under the rubric of multimedia annotation. After all, the annotation modality is arbitrary; e.g. images can be annotated with text, text can be annotated with images, images can be annotated with audio, videos can be annotated with structured ontological descriptors. Techniques for carrying out appropriate annotation cover the gamut of present-day research topics in the area of multimedia semantics, including the following important areas: media object representation, genre representation and detection, event representation and detection, multimedia ontology learning, emergent semantics, and folksonomies. Media object representation is quite important, as particular representations lend themselves better to cross-media annotations. In the past, the different media were isolated and analysed separately. For example, images had one representation, audio another, and they were rarely analysed together. More and more researchers now realise that documents of interest are multimedia, not monomedia, and thus should be represented in an integrated fashion to take advantage of various mathematical techniques for discovering latent semantics (Zhao and Grosky 2002a). A multimedia document is represented as a high-dimensional vector, where the coordinates represent particular feature values. Using various dimensional reduction techniques, researchers have distilled various concepts from multimedia document collections, where each concept represents the co-occurrence of various elementary features. If, for example, an image feature and a textual feature occur in the same concept, we may be able to say that the textual feature is an annotation for the occurrence of the particular image feature (and vice versa).

Searching an appropriate template size for multimodal image clustering

It has been shown by researchers that using a multimodality approach can help in identifying better clusters in an image collection. The multimodal image features include low-level image features and available text annotations. This approach helps in identifying inherent relationships among different types of features associated with an image. In our approach, we divide images into small tiles and create visual keywords using a high-dimensional clustering algorithm. These visual keywords act the same as text keywords. One of the challenges of this approach is to identify an appropriate size for visual keywords. In this paper, we report our results in finding a suitable template size that can be used to create tiles for visual keywords. These visual keywords are combined with text keywords to create a multimodal image representation before applying clustering.

On discovering most frequent research trends in a scientific discipline using a text mining technique

Science encompasses many sub-domains such as Computer Science, Physics, Medicine, etc. Each domain such as Computer Science can have many sub-disciplines like Networking, Data Mining, Parallel Processing, etc. Many different techniques have been used in these disciplines to solve open problems and improve existing solutions. Innovations in techniques call for researching prevalent solutions and active work being done in that field. It is thus highly desirable, yet a challenging task to automate the process of identifying current trend of research in any sub-discipline. Automation techniques will allow for faster exploration of methodology and ideas, especially among young researchers or the ones switching to related disciplines, enabling further improvisation and invention. This paper presents a technique for mining the titles and abstracts of research papers to aid in achieving this task. The key idea behind this is that the title and abstract of a research paper encompass within their component words, the core technique, methodology, or aim of that paper. We thus present preliminary ideas of a text mining technique that can efficiently identify trending research topics in a discipline. Our initial experiments exhibit encouraging results.

Analyzing security threats as reported by the United States Computer Emergency Readiness Team (US-CERT)

The 21st century has seen an enormous and almost sudden expansion in the use and types of technology. However, the cyber-age has also brought along with it new ways to wage attacks which has been a daunting task to thwart. This paper analyzes the number of high-impact security threats, vulnerabilities and alerts that have been reported by the United States Computer Emergency Readiness Team (US-CERT) over the past five years. This paper also closely examines the companies with the highest numbers of reports.