An emergency response process outlines the workflow of different activities that need to be performed in response to an emergency. Effective emergency response requires communication and coordination with the operational systems belonging to different collaborating organizations. Therefore, it is necessary to establish information sharing and system-level interoperability among the diverse operational systems. Unlike typical e-government processes that are well structured and have a well-defined outcome, emergency response processes are knowledge-centric and their workflow structure and execution may evolve as the incident unfolds. It is impractical to define static plans and response process workflows for every possible situation. Instead, a dynamic response should be adaptable to the changing situation. We present an integrated approach that facilitates the dynamic composition of an executable response process. The proposed approach employs ontology-based reasoning to determine the default actions and resource requirements for the given incident and to identify relevant response organizations based on their jurisdictional and mutual aid agreement rules. The Web service APIs of the identified response organizations are then used to generate an executable response process that evolves dynamically. The proposed approach is implemented and experimentally validated using an example scenario derived from the FEMA Hazardous Materials Tabletop Exercises Manual.

Development of a Business Process (BP) is a challenging task for small and medium enterprises (SMEs) which often do not have adequate resources for design, coding, and management of their BPs. Knowledge of existing BPs of related organizations can be exploited for collaborative BP development. However, syntactic and semantic heterogeneity among the Web service operations of BPs across organizations is a major obstacle to such collaborative BP development. In this paper, we propose an approach for collaborative BP development that exploits the attribute and structural similarity of related BPs as well as the semantic information including preconditions and postconditions of operations, to compute a mapping between the available service operations of the user organization and the BP operations of other organizations. We experimentally evaluate the approach with real world data from e-commerce sales BPs and demonstrate its effectiveness.

Business Process (BP) composition is a challenging task for small and medium organizations that do not have sufficient resources for design, coding, and management of their BPs. Cloud infrastructure and service-oriented middleware can be leveraged for rapid development and deployment of BPs of such organizations. BP development in the cloud-based environment can be done by exploiting the knowledge of existing BPs of related organizations. In this demonstration, we present the BP- Com tool which is a Web-based interactive system that enables efficient development of BPs in the cloud. BP-Com implements our service mapping approach called ASSEMBLE that utilizes the attribute, structural and semantics information of service operations of existing BPs in a given domain to help a user organization to compose its BP. Given a collection of related BPs and available service operations of a user organization, BP-Com computes a mapping between the available service operations of the user organization and the BP operations of other organizations. The results of operation mapping are presented to the user for refinement and customization of the generated BP workflow. Executable BP code is then generated in standard BPEL language, which can be deployed on any process execution engine on the user organization's site or on the cloud.

Comorbidity greatly increases the complexity of managing disease in patients. Approximately 27 percent of the US population have two or more concurrent comorbid conditions. Traditional models for assessing the impact of patient demographic and comorbidity burden on patient health outcomes, represented comorbidity conditions by Charlson Comorbidity Index. In this paper, we develop a novel two-way clustering approach combining model-based and weighted K-means clustering methods for characterizing and summarizing a patient's comorbid conditions. Our two-way approach helps reduce the size of the data to a manageable size, thus being practical for big data applications. Another novel aspect of our approach is the ability to handle weighted observations. Assigning weights to observations helps reduce the size of the dataset, thus addressing the scalability challenge of algorithms when dealing with big data. Using the National Inpatient Sample database for 2008-2013, we evaluate the performance of our approach by the use of logistic regression and support vector machine models by applying them to patients whose primary diagnosis is cardiovascular disease. In addition to evaluating our proposed method using empirical test data, we use asymptotic statistics. Both evaluation methods show that the proposed approach improves the prediction of patient health outcomes; specifically, hospital length of stay.

Outlier detection is one of the most important data analytics tasks and is used in numerous applications and domains. The goal of outlier detection is to find abnormal entities that are significantly different from the remaining data. Often the underlying data is distributed across different organizations. If outlier detection is done locally, the results obtained are not as accurate as when outlier detection is done collaboratively over the combined data. However, the data cannot be easily integrated into a single database due to privacy and legal concerns. In this paper, we address precisely this problem. We first define privacy in the context of collaborative outlier detection. We then develop a novel method to find outliers from both horizontally partitioned and vertically partitioned categorical data in a privacy-preserving manner. Our method is based on a scalable outlier detection technique that uses attribute value frequencies. We provide an end-to-end privacy guarantee by using the differential privacy model and secure multiparty computation techniques. Experiments on real data show that our proposed technique is both effective and efficient.

Collaborative business process composition exploits the knowledge of existing business processes of related organizations to compose an executable business process for a given organization based on its requirements and design specifications. Typically, this requires organizations to share and upload their existing business process execution sequences to a central repository. However, even after masking of confidential data, the execution sequences may still include sensitive business information which organizations may not want to share with their competitors. To address this issue, we develop a privacy-preserving Business Process Recommendation and Composition System (BPRCS), that generates a differentially private dataset of execution sequences which can be published and shared with other organizations for composition and implementation of their business processes. We also employ process mining and classification techniques on this differentially private dataset to regenerate the executable business process workflow. We experimentally validate the effectiveness of our approach.

A business process can be developed as a composition of Web services provided by different service providers. These service providers may have their own policies and constraints for service provisioning and collaboration. In this paper, we focus on secure composition of services, specifically from t

A vast majority of web services exist without explicit associated semantic descriptions. As a result many services that are relevant to a specific user service request may not be considered during service discovery. In this paper, we address the issue of web service discovery given nonexplicit service description semantics that match a specific service request. Our approach to semantic-based web service discovery involves semantic-based service categorization and semantic enhancement of the service request. We propose a solution for achieving functional level service categorization based on an ontology framework. Additionally, we utilize clustering for accurately classifying the web services based on service functionality. The semantic-based categorization is performed offline at the universal description discovery and integration (UDDI). The semantic enhancement of the service request achieves a better matching with relevant services. The service request enhancement involves expansion of additional terms (retrieved from ontology) that are deemed relevant for the requested functionality. An efficient matching of the enhanced service request with the retrieved service descriptions is achieved utilizing Latent Semantic Indexing (LSI). Our experimental results validate the effectiveness and feasibility of the proposed approach.

Graph structured data can be ubiquitously found in the real world. For example, social networks can easily be represented as graphs where the graph connotes the complex sets of relationships between members of social systems. While their analysis cou

Contingency tables are widely used in many fields to analyze the relationship or infer the association between two or more variables. Indeed, due to their simplicity and ease, they are one of the first methods used to analyze gathered data. Typically, the construction of contingency tables from sou rce data is considered straightforward since all data is supposed to be aggregated at a single party. However, in many cases, the collected data may actually be federated among different parties. While construction of the global contingency tables would still be of immense interest, privacy and security concerns may restrict the data owners from free sharing of the raw data. In this paper, we propose techniques for enabling secure construction of contingency tables from both horizontally and vertically partitioned data. Our methods are efficient and secure. We also examine cases where the constructed contingency table may itself leak too much information and discuss potential solutions. In order to protect certain sensitive cell values against being inferred from the marginal totals of a constructed contingency table, we further address the problem of how to securely publish the marginal totals.

Distributed and open environments require flexible, scalable and extendible trust verification mechanisms to access resources. To address this, the use of digital credentials as a means for making access decisions has been promoted. The resource owner needs to verify if the requester's credentials satisfy the security policy of the owner. However, such verification becomes a challenging problem when either the requester does not wish to disclose her credentials before the verification is complete, or the owner wishes to keep its security policy confidential from the requester, or both. In addition, the requester may associate a score to each of her credentials based on her perceived level of privacy. Earlier proposals to address this problem limit the owners policy to be a set of credentials. However, real world policies are more complex than a simple set. In this paper, we present three alternative privacy preserving trust verification solutions that protect both the owner's policy and requester's credentials, while at the same time allowing more expressive owner's policies that can be specified as a tree structure. We analyze their computational complexity, communication cost and the amount of disclosure.

Many real world phenomena can be naturally modeled as graph structures whose nodes representing entities and whose edges representing interactions or relationships between entities. The analysis of the graph data have many practical implications. However, the release of the data often poses considerable privacy risk to the individuals involved. In this paper, we address the edge privacy problem in graphs. In particular, we explore random perturbation for privacy preservation in graph data, and propose an iterative derivation process to analyze node reachability within the graph. We specifically focus on deriving the probability that the shortest path linking two nodes in a directed graph is of a particular length. This allows us to determine the expected length of the shortest path between two nodes, and determine whether they are linked or not. The performance of the proposed method is demonstrated via extensive experiments on both real and synthetic datasets.

User search query logs have proven to be very useful, but have vast potential for misuse. Several incidents have shown that simple removal of identifiers is insufficient to protect the identity of users. Publishing such inadequately anonymized data can cause severe breach of privacy. While significant effort has been expended on coming up with anonymity models and techniques for microdata, there is little corresponding work for query log data. Query logs are different in several important aspects, such as the diversity of queries and the causes of privacy breach. This necessitates the need to design privacy models and techniques specific to this environment. This paper takes a first cut at tackling this challenge. Our main contribution is to define effective anonymization models for query log data along with proposing techniques to achieve such anonymization. We analyze the inherent utility and privacy tradeoff, and experimentally validate the performance of our techniques.

Graph structured data can be ubiquitously found in the real world. For example, social networks can easily be represented as graphs where the graph connotes the complex sets of relationships between members of social systems. While their analysis could be beneficial in many aspects, publishing certain types of social networks raises significant privacy concerns. This brings the problem of graph anonymization into sharp focus. Unlike relational data, the true information in graph structured data is encoded within the structure and graph properties. Motivated by this, we propose a structure aware anonymization approach that maximally preserves the structure of the original network as well as its structural properties while anonymizing it. Instead of anonymizing each node one by one independently, our approach treats each partitioned substructural component of the network as one single unit to be anonymized. This maximizes utility while enabling anonymization. We apply our method to both synthetic and real datasets and demonstrate its effectiveness and practical usefulness.

Devising a complete and correct set of roles has been recognized as one of the most important and challenging tasks in implementing role based access control. A key problem related to this is the notion of goodness - when is a set of roles good? Recently, the role mining problem (RMP) has been defined as the problem of discovering an optimal set of roles from existing user permissions. Several different objectives for optimality have been proposed. However, one problem with these definitions is that often organizations already have a deployed set of roles and wish to optimize this set. Even if an optimal set of roles is discovered, if this is widely different, it is impossible to simply throw out the deployed roles and start using the new ones as this may disrupt organizational processes and separation of duty constraints that are defined on roles. Essentially, what is missing is taking role migration cost into account when defining optimality, which would allow us to come up with the best suited set of roles.

In the context of homeland security, one of the key challenges in such environment is achieving effective, timely and orderly collaboration and information sharing among various government agencies at the Federal, state, and local levels. These agencies are using the Web as one of the means for sharing related information that is of different forms. In case of a crisis, a virtual response team needs to be formed in an ad-hoc manner. Members of this virtual response team come from various government agencies and private organizations. Depending on various factors, including the location and the nature of the crisis, the composition of this virtual multi- agency response team may change from one crisis to another. Furthermore, during the course of a given crisis the membership of this virtual multi-agency response team (VMART) may change dynamically to accommodate various needs (e.g., public health versus fire) and to confirm to certain constraints, such as jurisdictions, e.g., as the crisis extended from only New York, initially to New Jersey.

In an emergency situation failure to respond in a timely manner poses a significant threat. Data needed for timely response comes from various sources and sensors. These individual data streams when viewed in isolation may appear irrelevant, however, when analyzed collectively may identify potential threats. An effective and timely response also requires collaboration and information sharing among various government agencies at all levels. This collaboration information sharing among agencies can be achieved using service-oriented architecture, where agencies provide access to their information resources and applications using Web services. Each of these agencies has its own rules/policies for providing their services. It is therefore, important to verify the correctness of the emergency response processes with respect to the rules/policies of the collaborating agencies involved in the execution of such processes. In this paper we present an approach which addresses the above challenges. Specifically, the proposed approach: a) employs multi stream data mining for identification of potential threats and disambiguation of alarms; b) provides a methodology for the discovery and selection of relevant Web services; c) employs a timed automata based verification methodology for determining the correctness of emergency response processes with respect to the rules of the collaborating agencies. We provide an overview of the initial implementation of the proposed approach.

Semantic Web technology is a promising first step for automated Web service discovery. Most current approaches for Web service discovery cater to semantic Web services, i.e., Web services that have associated semantic descriptions. It is unrealistic, however, to expect all new services to have associated semantic descriptions. Furthermore, the descriptions of the vast majority of already existing services do not have explicitly associated semantics. In this paper we present a novel approach for Web service discovery that combines semantic and statistical association metrics. Semantic metrics are based on the semantic aspects of relevant ontology. Statistical association metrics are based on the association aspects of Web services instances (their inputs and outputs). Specifically, our approach exploits semantic relationship ranking for establishing semantic relevance, and a hyperclique pattern discovery method for grouping Web service parameters into meaningful associations. These associations combined by the semantic relevance are then leveraged to discover and rank Web services

Typically, high-resolution remote sensing (HRRS) images contain a high level noise as well as possess different texture scales. As a result, existing image segmentation approaches are not suitable to HRRS imagery. In this paper, we have presented an unsupervised texture-based segmentation algorithm suitable for HRRS images, by extending the local binary pattern texture features and the lossless wavelet transform. Our experimental results using USGS 1ft or thoimagery show a significant improvement over the previously proposed LBP approach.

A composite multimedia object (cmo) is comprised of different media components such as text, video, audio and image, with a variety of constraints that must be adhered to. The constraints are 1) rendering constraints that comprise the temporal and spatial constraints between different components, and 2) behavioral constraints that include the security and fidelity constraints on each component. Different users have different 3Cs, which are: capabilities (e.g., monitor size), characteristics (e.g., age) and credentials (e.g., subscription to service). The focus of this paper is on addressing the problems of (1) specifying a consistent cmo that "automatically" adapts its spatial constraints to different user's devices. (2) Identifying the conflicts that might occur between the temporal and spatial constraints when having different monitor resolution that displays the cmo by means of reachability analysis of colored time Petri net (3) Resolving the identified conflicts automatically to render a cmo that is error-free when rendered at different user devices.

A key challenge for Mobile services is to offer personalized contents while preserving the privacy of customers. In mobile applications, location information is modeled as moving objects. Providing proper protection to customer information can be achieved by an access control system. However, providing such system is a challenging task due to: 1) the spatio-temporal nature of the constraints as well as the location information, and the interaction among them; 2) the complexity of resolving spatio-temporal and granularity conflicts; and 3) the required scalability and efficiency. In this paper, we present a solution that includes an access control model for moving objects and customer profiles. We also present a mechanism that enforces the spatio-temporal policies. The mechanism consists of three components: a text encoder, a spatio-temporal module that computes interactions between moving objects and spatio-temporal constraints, and a new data structure referred to as the Adaptive Search Multi-way trie (ASM-trie). We present the insertion and search algorithms of the ASM-trie and an evaluation study that shows the positive impact of the ASM-trie on the search efficiency.

A composite multimedia object (cmo) is comprised of different media components such as text, video, audio and image, with a variety of constraints that must be adhered to. The constraints are 1) rendering relationships that comprise the temporal and spatial constraints between different components, 2) behavioral requirements that include the security and fidelity constraints on each component and, 3) user interactions on a set of related media components. Different users have different capabilities (e.g. age), characteristics (e.g. monitor size) and credentials (e.g. subscription to service). Our objective is to author an interactive adaptive cmo that renders itself correctly to different users. Therefore, it is important to guarantee the consistency of the cmo specifications in all possible scenarios. In this paper, we include the user interaction with temporal and spatio-temporal behavior in the specification of the adaptive cmo. We then check the consistency of user interaction specifications by transforming the specifications into a color time Petri net model. We perform a reachability analysis on the Petri net to identify inconsistencies. We then resolve the identified inconsistencies to have a consistent Petri net. A consistent Petri net presents an error-free interactive cmo that can adapt to different users, by guaranteeing that link user interactions are reachable for all eligible users.

Digital libraries (DLs) introduce several challenging requirements with respect to the formulation, specification and enforcement of adequate data protection policies. Unlike conventional database environments, a DL environment is typically characterized by a dynamic user population, often making accesses from remote locations, and by an extraordinarily large amount of multimedia information, stored in a variety of formats. Moreover, in a DL environment, access policies are often specified based on user qualifications and characteristics, rather than on user identity (e.g. a user can be given access to an R-rated video only if he/ she is more than 18 years old). Another crucial requirement is the support for content-dependent authorizations on digital library objects (e.g. all documents containing discussions on how to operate guns must be made available only to users who are 18 or older). Since traditional authorization models do not adequately meet the access control requirements typical of DLs, we propose a content-based authorization model that is suitable for a DL environment. Specifically, the most innovative features of our authorization model are: (1) flexible specification of authorizations based on the qualifications and (positive and negative) characteristics of users, (2) both content-dependent and content-independent access control to digital library objects, and (3) the varying granularity of authorization objects ranging from sets of library objects to specific portions of objects.

The paper describes novel methods for classification in order to find an optimal tree. Unlike the current splitting rules that are provided by searching all threshold values, the paper proposes splitting rules that are based on the probabilities of pre-assigned intervals. In experiments, we demonstrate that our methods properly classify image objects based on new split rules.

Digital libraries are concerned with the creation and management of information sources, the movement of information across global networks, and the effective use of this information by a wide range of users. A digital library is a vast collection of objects that are of multimedia nature, e.g. text, video, images, and audio. Users wishing to access the digital library objects may possess varying capabilities, preferences, domain expertise, and may use different information appliances. Facilitating access to complex multimedia digital library objects that suits the users' requirements is known as universal access. We present an object manifestation approach in which digital library objects automatically manifest themselves to cater to the users' capabilities and characteristics. We provide a formal framework, based on Petri nets, to represent the various components of the digital library objects, their modality and fidelity, and the playback synchronization relationships among them. We develop methodologies for object delivery without any deadtime under network delays.

As we embark on the information age the use of electronic information is spreading through all sectors of society, both nationally and internationally. As a result, commercial organizations, educational institutions and government agencies are finding it essential to be linked by world wide networks, and commercial Internet usage is growing at an accelerating pace.

The paper presents a methodology for developing a user interface that combines fourth generation interface tools (SQL forms) with a natural language processor for a database management system. The natural language processor consists of an index, a lexicon and a parser. The index is used to uniquely identify each form in the system through a conceptual representation of its purpose. The form fields specify database or nondatabase fields whose values are either entered by the user (user-defined) or are derived by the form (system-defined) in response to user input. A set of grammar rules are associated with each form. The lexicon consists of all words recognized by the system, their grammatical categories, roots, their associations (if any) with database objects and forms. The parser scans, a natural language query to identify a form in a bottom-up fashion. The information requested in the user query is determined in a top-down manner by parsing, through the grammar rules associated with the identified form. Extragrammatical inputs with limited deviations from the grammar rules are supported. Combining a natural language processor with SQL forms allows processing data modification tasks without violating any database integrity constraint, having duplicate records, or entering invalid data. A prototype natural language interface is described as a front-end to an ORACLE database for a computer integrated manufacturing system.

We consider the problem of keeping a distributed database system that has been partitioned because of site or communication link failures partially operable while ensuring data consistency. A dynamic-voting-consistency algorithm is proposed, and its correctness is demonstrated. The proposed algorithm results in improved efficiency in executing read requests by not requiring a read quorum. This algorithm is effective in environments where the majority of user requests are "read" types of requests. Furthermore, the proposed algorithm results in efficient recovery by avoiding updating those data objects that are still current. Under the proposed algorithm, the majority partition would be available even if changes in the network topology take place at a higher rate than the update rate, as long as only simple partitioning takes place.>

A structured methodology for linking data modeling with functional modeling in a computer integrated manufacturing system is presented. The target application, the functional and data models, and a method for developing the data model starting from the functional model are described. This approach ensures that the data model is complete and non-redundant with respect to the functional model. A scheme that enables various functions in the functional model to be linked with the data elements of the data model is also presented. Such a linkage makes it possible to determine the impact of a change in the functional model on the data model and vice versa.>

This paper deals with query processing using semantic knowledge in relational databases. The Select-Project-Join (SPJ) conjunctive class of queries are dealt with in this paper. We propose to optimize highly repetitive queries by using semantic transformations in addition to syntactic transformations. Thus, we generate a set of pre-optimized queries. This set contains queries that are semantically equivalent to, syntactically different from, and more efficient to process than the user queries that we started with. The issues we address in this paper are: how to map a user query to a query that is in the set of pre-optimized and already optimized queries, how to search efficiently through the set of pre-optimized queries and set of semantic rules, and how to incorporate new queries to the set of pre-optimized queries, so that the number of queries that can be optimized using this method increases with the passage of time. Furthermore, we suggest some ideas of handling queries that do not have any semantically equivalent counterpart in the set of pre-optimized queries. We have tested the performance of the proposed method. An algorithm for mapping is implemented in Prolog. A database schema is implemented in the INGRES database management system. We have adopted a database schema that is widely used for measuring performance in the semantic query optimization literature.

The authors consider the consistency control problem for replicated data in a distributed computing system (DCS) and propose a new algorithm to dynamically regenerate copies of data objects in response to node failures and network partitioning in the system. The DCS is assumed to have strict consistency constraints for data object copies. The algorithm combines the advantages of voting-based algorithms and regeneration mechanisms to maintain mutual consistency of replicated data objects in the case of node failures and network partitioning. The algorithm extends the feasibility of regeneration to DCS on wide area networks and is able to satisfy user queries as long as there is one current partition in the system. A stochastic availability analysis of the algorithm shows that it provides improved availability as compared to previously proposed dynamic voting algorithms.>

The resource allocation problem in distributed computing systems that have strict mutual consistency requirements is discussed. A model that incorporates the behavior of consistency control algorithms which ensure that mutual consistency of replicated data is preserved even when communication links of the computer network and/or computers on which the files reside fail is presented. The constrained resource allocation problem is formulated as a mixed nonlinear integer program. An efficient algorithm is proposed to solve this problem. The performance of the algorithm is evaluated in terms of its accuracy, efficiency and execution times, using a representative problem set.>

The consistency control problem for replicated data in a distributed computing system (DCS) is considered. An algorithm is proposed to dynamically regenerate copies of data objects in response to node failures and network partitioning in the system. The DCS is assumed to have strict consistency constraints for data object copies. The algorithm combines the advantages of voting-based algorithms and regeneration mechanisms to maintain mutual consistency of replicated data objects in the case of node failures and network partitioning. The algorithm extends the feasibility of regeneration to DCS on wide-area networks (WANs), and is able to satisfy user queries as long as there is one current partition in the system.>