OS and Networks Commons^™

Biomedical Ontologies For Parasite Research, Vinh Nguyen, Satya S. Sahoo, Priti Parikh, Todd Minning, Brent Weatherly, Flora Logan, Amit P. Sheth, Rick Tarleton

Kno.e.sis Publications

Trypanosoma cruzi is a protozoan parasite that causes Chagas disease or American trypanosomiasis, which is the leading cause of death in Latin America. The primary objective of this study is to create an ontology-driven information infrastructure to support parasite researchers in identifying gene knockout, vaccination, or drug targets for T. cruzi. This involves querying across multiple datasets from diverse sources, such as proteome, pathway, internal lab data, etc. that are often represented in heterogeneous formats. To address this, a multi-ontology parasite knowledge repository (PKR) is being created with an intuitive graphical query interface called Cuebee. The PKR is underpinned by …

Cloud Based Scientific Workflow For Nmr Data Analysis, Ashwin Manjunatha, Paul E. Anderson, Satya S. Sahoo, Ajith Harshana Ranabahu, Michael L. Raymer, Amit P. Sheth

Kno.e.sis Publications

This work presents a service oriented scientific workflow approach to NMR-based metabolomics data analysis. We demonstrate the effectiveness of this approach by implementing several common spectral processing techniques in the cloud using a parallel map-reduce framework, Hadoop.

How To Make Linked Data More Than Data, Prateek Jain, Amit P. Sheth, Kunal Verma, Pascal Hitzler, Peter Z. Yeh

Kno.e.sis Publications

The LOD cloud has a potential for applicability in many AI-related tasks, such as open domain question answering, knowledge discovery, and the Semantic Web. An important prerequisite before the LOD cloud can enable these goals is allowing its users (and applications) to effectively pose queries to and retrieve answers from it. However, this prerequisite is still an open problem for the LOD cloud and has restricted it to 'merely more data.' To transform the LOD cloud from 'merely more data' to 'semantically linked data' there are plenty of open issues which should be addressed. We believe this transformation of the …

Semantically Annotated Restful Services For Large-Scale Metabolomics Data Analysis, Ashwin Manjunatha, Paul E. Anderson, Satya S. Sahoo, Ajith H. Ranabahu, Michael L. Raymer, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

Janus: From Workflows To Semantic Provenance And Linked Open Data, Paolo Missier, Satya S. Sahoo, Jun Zhao, Carole Goble, Amit P. Sheth

Kno.e.sis Publications

Data provenance graphs are form of metadata that can be used to establish a variety of properties of data products that undergo sequences of transformations, typically specified as workflows. Their usefulness for answering user provenance queries is limited, however, unless the graphs are enhanced with domain-specific annotations. In this paper we propose a model and architecture for semantic, domain-aware provenance, and demonstrate its usefulness in answering typical user queries. Furthermore, we discuss the additional benefits and the technical implications of publishing provenance graphs as a form of Linked Data. A prototype implementation of the model is available for data produced …

Provenance Management In Parasite Research, Vinh Nguyen, Priti Parikh, Satya S. Sahoo, Amit P. Sheth

Kno.e.sis Publications

The objective of this research is to create a semantic problem solving environment (PSE) for human parasite Trypanosoma cruzi. As a part of the PSE, we are trying to manage provenance of the experiment data as it is generated. It requires to capture the provenance which is often collected through web forms used by biologists to input the information about experiments they conduct. We have created Parasite Experiment Ontology (PEO) that represents provenance information used in the project. We have modified the back end which processes the data gathered from biologists, generates RDF triples and serializes them into the triple …

Some Trust Issues In Social Networks And Sensor Networks, Krishnaprasad Thirunarayan, Pramod Anantharam, Cory Andrew Henson, Amit P. Sheth

Kno.e.sis Publications

Trust and reputation are becoming increasingly important in diverse areas such as search, e-commerce, social media, semantic sensor networks, etc. We review past work and explore future research issues relevant to trust in social/sensor networks and interactions. We advocate a balanced, iterative approach to trust that marries both theory and practice. On the theoretical side, we investigate models of trust to analyze and specify the nature of trust and trust computation. On the practical side, we propose to uncover aspects that provide a basis for trust formation and techniques to extract trust information from concrete social/sensor networks and interactions. We …

Linked Sensor Data, Harshal Kamlesh Patni, Cory Andrew Henson, Amit P. Sheth

Kno.e.sis Publications

A number of government, corporate, and academic organizations are collecting enormous amounts of data provided by environmental sensors. However, this data is too often locked within organizations and underutilized by the greater community. In this paper, we present a framework to make this sensor data openly accessible by publishing it on the Linked Open Data (LOD) Cloud. This is accomplished by converting raw sensor observations to RDF and linking with other datasets on LOD. With such a framework, organizations can make large amounts of sensor data openly accessible, thus allowing greater opportunity for utilization and analysis.

Trust In Social And Sensor Networks, Pramod Anantharam, Krishnaprasad Thirunarayan, Cory Andrew Henson, Amit P. Sheth

Kno.e.sis Publications

Trust can be defined as the perception of the trustor about the degree to which the trustee would satisfy an expectation about a transaction constituting risk. Trust plays a pivotal role when the risk in believing incorrect information is high. With Web 2.0 where user generated content and real time interactions dominate, the openness of data contribution may hinder the quality of information we can get.

What Goes Around Comes Around - Improving Linked Open Data Through On-Demand Model Creation, Christopher Thomas, Wenbo Wang, Pankaj Mehra, Delroy H. Cameron, Pablo N. Mendes, Amit P. Sheth

Kno.e.sis Publications

Web 2.0 has changed the way we share and keep up with information. We communicate through social media platforms and make the information we exchange to a large extent publicly available. Linked Open Data (LOD) follows the same paradigm of sharing information but also makes it machine accessible. LOD provides an abundance of structured information albeit in a less formally rigorous form than would be desirable for Semantic Web applications. Nevertheless, most of the LOD assertions are community reviewed and we can rely on their accuracy to a large extent. In this work we want to follow the Web 2.0 …

Dynamic Associative Relationships On The Linked Open Data Web, Pablo N. Mendes, Pavan Kapanipathi, Delroy H. Cameron, Amit P. Sheth

Kno.e.sis Publications

We provide a definition of context based on theme, time and location, and propose a mixed retrieval/extraction model for the dynamic suggestion of trending relationships to LOD resources.

Semantics-Empowered Text Exploration For Knowledge Discovery, Delroy H. Cameron, Pablo N. Mendes, Amit P. Sheth, Victor Chan

Kno.e.sis Publications

The interaction paradigm offered by most contemporary Web Information Systems is a search-and-sift paradigm in which users manually seek information using hyperlinked documents. This paradigm is derived from a document-centric model that gives users minimal support for scanning through high volumes of text. We present a novel information exploration paradigm based on a data-centric view of corpora, along with a prototype implementation that demonstrates the value in content-driven navigation. We leverage semantic metadata to link data in documents by exploiting named relationships between entities. We also present utilities for gathering user generated navigation trails, critical for knowledge discovery. We discuss …

Understanding Events Through Analysis Of Social Media, Amit P. Sheth, Hemant Purohit, Ashutosh Sopan Jadhav, Pavan Kapanipathi, Lu Chen

Kno.e.sis Publications

Users are sharing vast amounts of social data through social networking platforms accessible by Web and increasingly via mobile devices. This opens an exciting opportunity to extract social perceptions as well as obtain insights relevant to events around us. We discuss the significant need and opportunity for analyzing event-centric user generated content on social networks, present some of the technical challenges and our approach to address them. This includes aggregating social data related to events of interest, along with Web resources (news, Wikipedia pages, multimedia) related to an event of interest, and supporting analysis along spatial, temporal, thematic, and sentiment …

Mobicloud - Making Clouds Reachable: A Toolkit For Easy And Efficient Development Of Customized Cloud Mobile Hybrid Applications, Ashwin Manjunatha, Ajith Harshana Ranabahu, Amit P. Sheth, Krishnaprasad Thirunarayan

Kno.e.sis Publications

The advancements in computing have resulted in a boom of cheap, ubiquitous, connected mobile devices, as well as seemingly unlimited, utility style, pay as you go computing resources, commonly referred to as Cloud computing. However, taking full advantage of this mobile and cloud computing landscape, especially for the data intensive domains, has been hampered by the many heterogeneities that exist in the mobile space, as well as the Cloud space. Our research attempts to exploit the capabilities of the mobile and cloud landscape by introducing MobiCloud, an online toolkit to efficiently develop Cloud-mobile hybrid (CMH) applications. We define a CMH …

Scale: A Scalable Framework For Efficiently Clustering Transactional Data, Hua Yan, Keke Chen, Ling Liu, Zhang Yi

Kno.e.sis Publications

This paper presents SCALE, a fully automated transactional clustering framework. The SCALE design highlights three unique features. First, we introduce the concept of Weighted Coverage Density as a categorical similarity measure for efficient clustering of transactional datasets. The concept of weighted coverage density is intuitive and it allows the weight of each item in a cluster to be changed dynamically according to the occurrences of items. Second, we develop the weighted coverage density measure based clustering algorithm, a fast, memory-efficient, and scalable clustering algorithm for analyzing transactional data. Third, we introduce two clustering validation metrics and show that these domain …

From Questions To Effective Answers: On The Utility Of Knowledge-Driven Querying Systems For Life Sciences Data, Amir H. Asiaee, Prashant Doshi, Todd Minning, Satya S. Sahoo, Priti Parikh, Amit P. Sheth, Rick L. Tarleton

Kno.e.sis Publications

We compare two distinct approaches for querying data in the context of the life sciences. The first approach utilizes conventional databases to store the data and intuitive form-based interfaces to facilitate easy querying of the data. These interfaces could be seen as implementing a set of 'pre-canned' queries commonly used by the life science researchers that we study. The second approach is based on semantic Web technologies and is knowledge (model) driven. It utilizes a large OWL ontology and same datasets as before but associated as RDF instances of the ontology concepts. An intuitive interface is provided that allows the …

Sensor Discovery On Linked Data, Josh Pschorr, Cory Andrew Henson, Harshal Kamlesh Patni, Amit P. Sheth

Kno.e.sis Publications

There has been a drive recently to make sensor data accessible on the Web. However, because of the vast number of sensors collecting data about our environment, finding relevant sensors on the Web is a non-trivial challenge. In this paper, we present an approach to discovering sensors through a standard service interface over Linked Data. This is accomplished with a semantic sensor network middleware that includes a sensor registry on Linked Data and a sensor discovery service that extends the OGC Sensor Web Enablement. With this approach, we are able to access and discover sensors that are positioned near named-locations …

Sensor Data And Perception: Can Sensors Play 20 Questions, Cory Andrew Henson

Kno.e.sis Publications

Currently, there are many sensors collecting information about our environment, leading to an overwhelming number of observations that must be analyzed and explained in order to achieve situation awareness. As perceptual beings, we are also constantly inundated with sensory data, yet we are able to make sense of our environment with relative ease. Why is the task of perception so easy for us, and so hard for machines; and could this have anything to do with how we play the game 20 Questions?

A Qualitative Examination Of Topical Tweet And Retweet Practices, Meenakshi Nagarajan, Hemant Purohit, Amit P. Sheth

Kno.e.sis Publications

This work contributes to the study of retweet behavior on Twitter surrounding real-world events. We analyze over a million tweets pertaining to three events, present general tweet properties in such topical datasets and qualitatively analyze the properties of the retweet behavior surrounding the most tweeted/viral content pieces. Findings include a clear relationship between sparse/dense retweet patterns and the content and type of a tweet itself; suggesting the need to study content properties in link-based diffusion models.

Provenance Aware Linked Sensor Data, Harshal Kamlesh Patni, Satya S. Sahoo, Cory Andrew Henson, Amit P. Sheth

Kno.e.sis Publications

Provenance, from the French word “provenir”, describes the lineage or history of a data entity. Provenance is critical information in the sensors domain to identify a sensor and analyze the observation data over time and geographical space. In this paper, we present a framework to model and query the provenance information associated with the sensor data exposed as part of the Web of Data using the Linked Open Data conventions. This is accomplished by developing an ontology-driven provenance management infrastructure that includes a representation model and query infrastructure. This provenance infrastructure, called Sensor Provenance Management System (PMS), is …

Getting Code Near The Data: A Study Of Generating Customized Data Intensive Scientific Workflows With Domain Specific Language, Ashwin Manjunatha, Ajith Harshana Ranabahu, Paul E. Anderson, Amit P. Sheth

Kno.e.sis Publications

The amount of data produced in modern biological experiments such as Nuclear Magnetic Resonance (NMR) analysis far exceeds the processing capability of a single machine. The present state-of-the-art is taking the ”data to code”, the philosophy followed by many of the current service oriented workflow systems. However this is not feasible in some cases such as NMR data analysis, primarily due to the large scale of data.

The objective of this research is to bring ”code to data”, preferred in the cases when the data is extremely large. We present a DSL based approach to develop customized data intensive scientific …

Loqus: Linked Open Data Sparql Querying System, Prateek Jain, Kunal Verma, Peter Z. Yeh, Pascal Hitzler, Amit P. Sheth

Kno.e.sis Publications

The LOD cloud is gathering a lot of momentum, with the number of contributors growing manifold. Many prominent data providers have submitted and linked their data to other dataset with the help of manual mappings. The potential of the LOD cloud is enormous ranging from challenging AI issues such as open domain question answering to automated knowledge discovery. We believe that there is not enough technology support available to effectively query the LOD cloud. To this effect, we present a system called Linked Open Data SPARQL Querying System (LOQUS), which automatically maps users queries written in terms of a conceptual …

Computing For The Human Experience: Semantics-Empowered Sensors, Services, And Social Computing On The Ubiquitous Web, Amit P. Sheth

Kno.e.sis Publications

People are on the verge of an era in which the human experience can be enriched in ways they couldn't have imagined two decades ago. Rather than depending on a single technology, people progressed with several whose semantics-empowered convergence and integration will enable us to capture, understand, and reapply human knowledge and intellect. Such capabilities will consequently elevate our technological ability to deal with the abstractions, concepts, and actions that characterize human experiences. This will herald computing for human experience (CHE). The CHE vision is built on a suite of technologies that serves, assists, and cooperates with humans to nondestructively …

Provenance Context Entity (Pace): Scalable Provenance Tracking For Scientific Rdf Data, Satya S. Sahoo, Olivier Bodenreider, Pascal Hitzler, Amit P. Sheth, Krishnaprasad Thirunarayan

Kno.e.sis Publications

The Resource Description Framework (RDF) format is being used by a large number of scientific applications to store and disseminate their datasets. The provenance information, describing the source or lineage of the datasets, is playing an increasingly significant role in ensuring data quality, computing trust value of the datasets, and ranking query results. Current provenance tracking approaches using the RDF reification vocabulary suffer from a number of known issues, including lack of formal semantics, use of blank nodes, and application-dependent interpretation of reified RDF triples. In this paper, we introduce a new approach called Provenance Context Entity (PaCE) that uses …

A Study In Hadoop Streaming With Matlab For Nmr Data Processing, Kalpa Gunaratna, Paul E. Anderson, Ajith Harshana Ranabahu, Amit P. Sheth

Kno.e.sis Publications

Applying Cloud computing techniques for analyzing large data sets has shown promise in many data-driven scientific applications. Our approach presented here is to use Cloud computing for Nuclear Magnetic Resonance (NMR)data analysis which normally consists of large amounts of data. Biologists often use third party or commercial software for ease of use. Enabling the capability to use this kind of software in a Cloud will be highly advantageous in many ways. Scripting languages especially designed for clouds may not have the flexibility biologists need for their purposes. Although this is true, they are familiar with special software packages that allow …

Twitris 2.0 : Semantically Empowered System For Understanding Perceptions From Social Data, Ashutosh Sopan Jadhav, Hemant Purohit, Pavan Kapanipathi, Pramod Anantharam, Ajith H. Ranabahu, Vinh Nguyen, Pablo N. Mendes, Alan Gary Smith, Michael Cooney, Amit P. Sheth

Kno.e.sis Publications

We present Twitris 2.0, a Semantic Web application that facilitates understanding of social perceptions by Semantics-based processing of massive amounts of event-centric data. Twitris 2.0 addresses challenges in large scale processing of social data, preserving spatio-temporal-thematic properties. Twitris 2.0 also covers context based semantic integration of multiple Web resources and expose semantically enriched social data to the public domain. Semantic Web technologies enable the systematic integration and analysis abilities.

Power Of Clouds In Your Pocket: An Efficient Approach For Cloud Mobile Hybrid Application Development, Ashwin Manjunatha, Ajith Harshana Ranabahu, Amit P. Sheth, Krishnaprasad Thirunarayan

Kno.e.sis Publications

The advancements in computing have resulted in a boom of cheap, ubiquitous, connected mobile devices as well as seemingly unlimited, utility style, pay as you go computing resources, commonly referred to as Cloud computing. However, taking full advantage of this mobile and cloud computing landscape, especially for the data intensive domains has been hampered by the many heterogeneities that exist in the mobile space as well as the Cloud space. Our research focuses on exploiting the capabilities of the mobile and cloud landscape by defining a new class of applications called cloud mobile hybrid (CMH) applications and a Domain Specific …

Semantics Centric Solutions For Application And Data Portability In Cloud Computing, Ajith Harshana Ranabahu, Amit P. Sheth

Kno.e.sis Publications

Cloud computing has become one of the key considerations both in academia and industry. Cheap, seemingly unlimited computing resources that can be allocated almost instantaneously and pay-as-you-go pricing schemes are some of the reasons for the success of Cloud computing. The Cloud computing landscape, however, is plagued by many issues hindering adoption. One such issue is vendor lock-in, forcing the Cloud users to adhere to one service provider in terms of data and application logic. Semantic Web has been an important research area that has seen significant attention from both academic and industrial researchers. One key property of Semantic Web …

Automated Isolation Of Translational Efficiency Bias That Resists The Confounding Effect Of Gc(At)-Content, Douglas W. Raiford, Dan E. Krane, Travis E. Doom, Michael L. Raymer

Kno.e.sis Publications

Genomic sequencing projects are an abundant source of information for biological studies ranging from the molecular to the ecological in scale; however, much of the information present may yet be hidden from casual analysis. One such information domain, trends in codon usage, can provide a wealth of information about an organism's genes and their expression. Degeneracy in the genetic code allows more than one triplet codon to code for the same amino acid, and usage of these codons is often biased such that one or more of these synonymous codons is preferred. Detection of this bias is an important tool …

Linked Open Social Signals, Pablo N. Mendes, Alexandre Passant, Pavan Kapanipathi, Amit P. Sheth

Kno.e.sis Publications

In this paper we discuss the collection, semantic annotation and analysis of real-time social signals from micro-blogging data. We focus on users interested in analyzing social signals collectively for sensemaking. Our proposal enables flexibility in selecting subsets for analysis, alleviating information overload. We define an architecture that is based on state-of-the-art Semantic Web technologies and a distributed publish subscribe protocol for real time communication. In addition, we discuss our method and application in a scenario related to the health care reform in the United States.