OS and Networks Commons^™

Prom: A Semantic Web Framework For Provenance Management In Science, Satya S. Sahoo, Roger Barga, Amit P. Sheth, Krishnaprasad Thirunarayan, Pascal Hitzler

Kno.e.sis Publications

The eScience paradigm is enabling researchers to collaborate over the Web in virtual laboratories and conduct experiments on an industrial scale. But, the inherent variability in the quality and trust associated with eScience resources necessitates the use of provenance information describing the origin of an entity. Existing systems often model provenance using ambiguous terminology, have poor domain semantics and include modeling inconsistencies that hinders interoperability. Further, mere collection of provenance information is of little value without a well-defined and scalable query mechanism.

In this paper, we present 'PrOM', a framework that addresses both the modeling and querying issues in eScience …

The Impact Of Communication Medium On Team Performance Patterns, Pilar Pazos, Mario G. Beruvides

Engineering Management & Systems Engineering Faculty Publications

The increased impact of globalization and rapidly changing technologies are some of the biggest challenges facing 21st century organizations. Remote teams supported through computer-based systems have been identified as a key lever to help organizations adapt to this turbulent environment. This study sheds some light on how teams perform and adapt by evaluating performance and synergy over time for teams in two different settings: computer supported and face-to-face. Participants in the study were randomly assigned either to a computer-supported or a face-to face group. Each group met over three sessions to perform a series of tasks that were evaluated using …

Characterization Of 1h Nmr Spectroscopic Data And The Generation Of Synthetic Validation Sets, Paul E. Anderson, Michael L. Raymer, Benjamin J. Kelly, Nicholas V. Reo, Nicholas J. Delraso, Travis E. Doom

Kno.e.sis Publications

Motivation: Common contemporary practice within the nuclear magnetic resonance (NMR) metabolomics community is to evaluate and validate novel algorithms on empirical data or simplified simulated data. Empirical data captures the complex characteristics of experimental data, but the optimal or most correct analysis is unknown a priori; therefore, researchers are forced to rely on indirect performance metrics, which are of limited value. In order to achieve fair and complete analysis of competing techniques more exacting metrics are required. Thus, metabolomics researchers often evaluate their algorithms on simplified simulated data with a known answer. Unfortunately, the conclusions obtained on simulated data are …

User Interface Design, Moritz Stefaner, Sebastien Ferre, Saverio Perugini, Jonathan Koren, Yi Zhang

Computer Science Faculty Publications

As detailed in Chap. 1, system implementations for dynamic taxonomies and faceted search allow a wide range of query possibilities on the data. Only when these are made accessible by appropriate user interfaces, the resulting applications can support a variety of search, browsing and analysis tasks. User interface design in this area is confronted with specific challenges. This chapter presents an overview of both established and novel principles and solutions.

Service Level Agreement In Cloud Computing, Pankesh Patel, Ajith H. Ranabahu, Amit P. Sheth

Kno.e.sis Publications

Cloud computing that provides cheap and pay-as-you-go computing resources is rapidly gaining momentum as an alternative to traditional IT Infrastructure. As more and more consumers delegate their tasks to cloud providers, Service Level Agreements(SLA) between consumers and providers emerge as a key aspect. Due to the dynamic nature of the cloud, continuous monitoring on Quality of Service (QoS) attributes is necessary to enforce SLAs. Also numerous other factors such as trust (on the cloud provider) come into consideration, particularly for enterprise customers that may outsource its critical data. This complex nature of the cloud landscape warrants a sophisticated means of …

Determining The Best K For Clustering Transactional Datasets: A Coverage Density-Based Approach, Hua Yan, Keke Chen, Ling Liu

Kno.e.sis Publications

The problem of determining the optimal number of clusters is important but mysterious in cluster analysis. In this paper, we propose a novel method to find a set of candidate optimal number Ks of clusters in transactional datasets. Concretely, we propose Transactional-cluster-modes Dissimilarity based on the concept of coverage density as an intuitive transactional inter-cluster dissimilarity measure. Based on the above measure, an agglomerative hierachical clustering algorithm is developed and the Merge Dissimilarity Indexes, which are generated in hierachical cluster merging processes, are used to find the candidate optimal number Ks of clusters of transactional data. Our experimental results on …

Dynamic Adaptation Of Joint Transmission Power And Contention Window In Vanet, Danda B. Rawat, Gongjun Yan, Dimitrie C. Popescu, Michele C. Weigle, Stephan Olariu

Electrical & Computer Engineering Faculty Publications

In this paper, we propose an algorithm for joint adaptation of transmission power and contention window to improve the performance of vehicular network in a cross layer approach. The high mobility of vehicles in vehicular communication results in the change in topology of the Vehicular Ad-hoc Network (VANET) dynamically, and the communication link between two vehicles might remain active only for short duration of time. In order for VANET to make a connection for long time and to mitigate adverse effects due to high and fixed transmission power, the proposed algorithm adapts transmission power dynamically based on estimated local traffic …

The 4 X 4 Semantic Model: Exploiting Data, Functional, Non-Functional And Execution Semantics Across Business Process, Workflow, Partner Services And Middleware Services Tiers, Amit P. Sheth, Karthik Gomadam

Kno.e.sis Publications

Business processes in the global environment increasingly encompass multiple partners and complex, rapidly changing requirements. In this context it is critical that strategic business objectives align with and map accurately to systems that support flexible and dynamic business processes. To support the demanding requirements of global business processes, we propose a comprehensive, unifying 4 X 4 Semantic Model that uses Semantic Templates to link four tiers of implementation with four types of semantics. The four tiers are the Business Process Tier, the Workflow Enactment Tier, the Partner Services Tier, and the Middleware Services Tier. The four types of semantics are …

Semantic Sensor Web, Amit P. Sheth, Cory Henson, Krishnaprasad Thirunarayan

Kno.e.sis Publications

No abstract provided.

Capturing Workflow Event Data For Monitoring, Performance Analysis, And Management Of Scientific Workflows, Matthew Valerio, Satya S. Sahoo, Roger Barga, Jared Jackson

Kno.e.sis Publications

To effectively support real-time monitoring and performance analysis of scientific workflow execution, varying levels of event data must be captured and made available to interested parties. This paper discusses the creation of an ontology-aware workflow monitoring system for use in the Trident system which utilizes a distributed publish/subscribe event model. The implementation of the publish/subscribe system is discussed and performance results are presented.

Growing Fields Of Interest: Using An Expand And Reduce Strategy For Domain Model Extraction, Christopher Thomas, Pankaj Mehra, Roger Brooks, Amit P. Sheth

Kno.e.sis Publications

Domain hierarchies are widely used as models underlying information retrieval tasks. Formal ontologies and taxonomies enrich such hierarchies further with properties and relationships associated with concepts and categories but require manual effort; therefore they are costly to maintain, and often stale. Folksonomies and vocabularies lack rich category structure and are almost entirely devoid of properties and relationships. Classification and extraction require the coverage of vocabularies and the alterability of folksonomies and can largely benefit from category relationships and other properties. With Doozer, a program for building conceptual models of information domains, we want to bridge the gap between the vocabularies …

On Static And Dynamic Partitioning Behavior Of Large-Scale Networks, Zhongmei Yao, Derek Leonard, Xiaoming Wang, Dmitri Loguinov

Computer Science Faculty Publications

In this paper, we analyze the problem of network disconnection in the context of large-scale P2P networks and understand how both static and dynamic patterns of node failure affect the resilience of such graphs. We start by applying classical results from random graph theory to show that a large variety of deterministic and random P2P graphs almost surely (i.e., with probability 1 − o(1)) remain connected under random failure if and only if they have no isolated nodes. This simple, yet powerful, result subsequently allows us to derive in closed-form the probability that a P2P network develops isolated nodes, and …

Planning With Ifalcon: Towards A Neural-Network-Based Bdi Agent Architecture, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

This paper presents iFALCON, a model of BDI (beliefdesire-intention) agents that is fully realized as a selforganizing neural network architecture. Based on multichannel network model called fusion ART, iFALCON is developed to bridge the gap between a self-organizing neural network that autonomously adapts its knowledge and the BDI agent model that follows explicit descriptions. Novel techniques called gradient encoding are introduced for representing sequences and hierarchical structures to realize plans and the intention structure. This paper shows that a simplified plan representation can be encoded as weighted connections in the neural network through a process of supervised learning. A case …

A Neural Network Model For A Hierarchical Spatio-Temporal Memory, Kiruthika Ramanathan, Luping Shi, Jianming Li, Kian Guan Lim, Zhi Ping Ang, Chong Chong Tow

Research Collection School Of Computing and Information Systems

The architecture of the human cortex is uniform and hierarchical in nature. In this paper, we build upon works on hierarchical classification systems that model the cortex to develop a neural network representation for a hierarchical spatio-temporal memory (HST-M) system. The system implements spatial and temporal processing using neural network architectures. We have tested the algorithms developed against both the MLP and the Hierarchical Temporal Memory algorithms. Our results show definite improvement over MLP and are comparable to the performance of HTM.

Relationship Web: Trailblazing, Analytics And Computing For Human Experience, Amit P. Sheth

Kno.e.sis Publications

This panel presentation was give at the 27th International Conference on Conceptual Modeling (ER 2008), Barcelona, Spain, October 20-23, 2008.

An Ontology-Driven Semantic Mash-Up Of Gene And Biological Pathway Information: Application To The Domain Of Nicotine Dependence, Satya S. Sahoo, Olivier Bodenreider, Joni L. Rutter, Karen J. Skinner, Amit P. Sheth

Kno.e.sis Publications

Objectives: This paper illustrates how Semantic Web technologies (especially RDF, OWL, and SPARQL) can support information integration and make it easy to create semantic mashups (semantically integrated resources). In the context of understanding the genetic basis of nicotine dependence, we integrate gene and pathway information and show how three complex biological queries can be answered by the integrated knowledge base.

Methods: We use an ontology-driven approach to integrate two gene resources (Entrez Gene and HomoloGene) and three pathway resources (KEGG, Reactome and BioCyc), for five organisms, including humans. We created the Entrez Knowledge Model (EKoM), an information model in OWL …

Adapting Ranking Functions To User Preference, Keke Chen, Ya Zhang, Zhaohui Zheng, Hongyuan Zha, Gordon Sun

Kno.e.sis Publications

Learning to rank has become a popular method for web search ranking. Traditionally, expert-judged examples are the major training resource for machine learned web ranking, which is expensive to get for training a satisfactory ranking function. The demands for generating specific web search ranking functions tailored for different domains, such as ranking functions for different regions, have aggravated this problem. Recently, a few methods have been proposed to extract training examples from user clickthrough log. Due to the low cost of getting user preference data, it is attractive to combine these examples in training ranking functions. However, because of the …

Description Logic Reasoning With Decision Diagrams: Compiling Shiq To Disjunctive Datalog, Sebastian Rudolph

Kno.e.sis Publications

We propose a novel method for reasoning in the description logic SHIQ. After a satisfiability preserving transformation from SHIQ to the description logic ALCIb, the obtained ALCIb Tbox T is converted into an ordered binary decision diagram (OBDD) which represents a canonical model for T. This OBDD is turned into a disjunctive datalog program that can be used for Abox reasoning. The algorithm is worst-case optimal w.r.t. data complexity, and admits easy extensions with DL-safe rules and ground conjunctive queries.

Segmenting Brain Tumors Using Pseudo-Conditional Random Fields, Chi-Hoon Lee, Shaojun Wang, Albert Murtha, Matthew R.G. Brown, Russell Greiner

Kno.e.sis Publications

Locating Brain tumor segmentation within MR (magnetic resonance) images is integral to the treatment of brain cancer. This segmentation task requires classifying each voxel as either tumor or non-tumor, based on a description of that voxel. Unfortunately, standard classifiers, such as Logistic Regression (LR) and Support Vector Machines (SVM), typically have limited accuracy as they treat voxels as independent and identically distributed (iid). Approaches based on random fields, which are able to incorporate spatial constraints, have recently been applied to brain tumor segmentation with notable performance improvement over iid classifiers. However, previous random field systems involved computationally intractable …

A Faceted Classification Based Approach To Search And Rank Web Apis, Karthik Gomadam, Ajith Harshana Ranabahu, Meenakshi Nagarajan, Amit P. Sheth, Kunal Verma

Kno.e.sis Publications

Web application hybrids, popularly known as mashups, are created by integrating services on the Web using their APIs. Support for finding an API is currently provided by generic search engines or domain specific solutions such as Google and ProgrammableWeb. Shortcomings of both these solutions in terms of and reliance on user tags make the task of identifying an API challenging. Since these APIs are described in HTML documents, it is essential to look beyond the boundaries of current approaches to Web service discovery that rely on formal descriptions. In this work, we present a faceted approach to searching and ranking …

Semantics Enhanced Services: Meteor-S, Sawsdl And Sa-Rest, Amit P. Sheth, Karthik Gomadam, Ajith Harshana Ranabahu

Kno.e.sis Publications

Services Research Lab at the Knoesis center and the LSDIS lab at University of Georgia have played a significant role in advancing the state of research in the areas of workflow management, semantic Web services and service oriented computing. Starting with the METEOR workflow management system in the 90's, researchers have addressed key issues in the area of semantic Web services and more recently, in the domain of RESTful services and Web 2.0. In this article, we present a brief discussion on the various contributions of METEOR-S including SAWSDL, publication and discovery of semantic Web services, data mediation, dynamic configuration …

Cascade Rsvm In Peer-To-Peer Network, Hock Hee Ang, Vivekanand Gopalkrishnan, Steven C. H. Hoi, Wee Keong Ng

Research Collection School Of Computing and Information Systems

The goal of distributed learning in P2P networks is to achieve results as close as possible to those from centralized approaches. Learning models of classification in a P2P network faces several challenges like scalability, peer dynamism, asynchronism and data privacy preservation. In this paper, we study the feasibility of building SVM classifiers in a P2P network. We show how cascading SVM can be mapped to a P2P network of data propagation. Our proposed P2P SVM provides a method for constructing classifiers in P2P networks with classification accuracy comparable to centralized classifiers and better than other distributed classifiers. The proposed algorithm …

Challenges Of Creating A Knowledge-Based Society: Education & Research For India & Gujarat, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

Tcruzikb: Enabling Complex Queries For Genomic Data Exploration, Pablo N. Mendes, Bobby Mcknight, Amit P. Sheth, Jessica C. Kissinger

Kno.e.sis Publications

We developed a novel analytical environment to aid in the examination of the extensive amount of interconnected data available for genome projects. Our focus is to enable flexibility and abstraction from implementation details, while retaining the expressivity required for post-genomic research. To achieve this goal, we associated genomics data to ontologies and implemented a query formulation and execution environment with added visualization capabilities. We use ontology schemas to guide the user through the process of building complex queries in a flexible Web interface. Queries are serialized in SPARQL and sent to servers via Ajax. A component for visualization of the …

Text Analytics For Semantic Computing - The Good, The Bad And The Ugly, Meenakshi Nagarajan, Cartic Ramakrishnan, Amit P. Sheth

Kno.e.sis Publications

This tutorial was give at the Second IEEE International Conference on Semantic Computing Santa Clara, CA, USA - August 4-7, 2008.

Connectionist Model Generation: A First-Order Approach, Sebastian Bader, Pascal Hitzler, Steffen Holldobler

Computer Science and Engineering Faculty Publications

Knowledge-based artificial neural networks have been applied quite successfully to propositional knowledge representation and reasoning tasks. However, as soon as these tasks are extended to structured objects and structure-sensitive processes as expressed e.g., by means of first-order predicate logic, it is not obvious at all what neural-symbolic systems would look like such that they are truly connectionist, are able to learn, and allow for a declarative reading and logical reasoning at the same time. The core method aims at such an integration. It is a method for connectionist model generation using recurrent networks with feed-forward core. We show in this …

Mediatability: Estimating The Degree Of Human Involvement In Xml Schema Mediation, Karthik Gomadam, Ajith Harshana Ranabahu, Lakshmish Ramaswamy, Amit P. Sheth, Kunal Verma

Kno.e.sis Publications

Mediation and integration of data are significant challenges because the number of services on the Web, and heterogeneities in their data representation, continue to increase rapidly. To address these challenges we introduce a new measure, mediatability, which is a quantifiable and computable metric for the degree of human involvement in XML schema mediation. We present an efficient algorithm to compute mediatability and an experimental study to analyze how semantic annotations affect the ease of mediating between two schemas. We validate our approach by comparing mediatability scores generated by our system with user-perceived difficulty. We also evaluate the scalability of our …

An Xml-Based Approach To Handling Tables In Documents, Krishnaprasad Thirunarayan, Trivikram Immaneni

Kno.e.sis Publications

We explore application of XML technology for handling tables in legacy semi-structured documents. Specifically, we analyze annotating heterogeneous documents containing tables to obtain a formalized XML Master document that improves traceability (hence easing verification and update) and enables manipulation using XSLT stylesheets. This approach is useful when table instances far outnumber distinct table types because the effort required to annotate a table instance is relatively less compared to formalizing table processing that respects table’s semantics. This work is also relevant for authoring new documents with tables that should be accessible to both humans and machines.

Applications Of Voting Theory To Information Mashups, Alfredo Alba, Varun Bhagwan, Julia Grace, Daniel Gruhl, Kevin Haas, Meenakshi Nagarajan, Jan Pieper, Christine Robson, Nachiketa Sahoo

Kno.e.sis Publications

Blogs, discussion forums and social networking sites are an excellent source for people's opinions on a wide range of topics. We examine the application of voting theory to "information mashups" - the combining and summarizing of data from the multitude of often-conflicting sources. This paper presents an information mashup in the music domain: a Top 10 artist chart based on user comments and listening behavior from several Web communities. We consider different voting systems as algorithms to combine opinions from multiple sources and evaluate their effectiveness using social welfare functions. Different voting schemes are found to work better in some …

Dependence Of Binary Associations On Co-Occurrence Granularity In News Documents, Krishnaprasad Thirunarayan, Trivikram Immaneni, Mastan Vali Shaik

Kno.e.sis Publications

We describe and formalize an approach to correlate binary associations (such as between entities and events, between persons and events, etc.) implied by News documents on the co-occurrence granularity (such as document-level, paragraph-level, sentence-level, etc.) of the corresponding text phrases in the documents. Specifically, we present both qualitative and quantitative characterization of searching News documents: former in terms of the nature of the content and the queries, and latter in terms of a metric obtained by adapting the notions of precision and recall. Specifically, the approach tries to reduce the manual effort required to analyze the News documents to compare …