The rapid advancement of Large Language Models (LLMs) has enabled them to generate complex, multi-step plans and itineraries. However, these generated plans often lack temporal and spatial consistency, particularly in scenarios involving physical travel constraints. This research aims to study the temporal performance of different LLMs and presents a validation framework that evaluates and improves the temporal consistency of LLM-generated travel itineraries. The system employs multiple state-of-the-art LLMs to generate travel plans and validates them against real-world flight duration constraints using the AeroDataBox API. This work contributes to the understanding of LLM capabilities in handling complex temporal reasoning tasks like itinerary generation and provides a framework to rectify any temporal inconsistencies like overlapping journeys or unrealistic transit times in the itineraries generated by LLMs before the itinerary is given to the user. Our experiments reveal that while current LLMs frequently produce temporally inconsistent itineraries, these can be systematically and reliably corrected using our framework, enabling their practical deployment in large-scale travel planning.

Existing methods to manage privileges in smart home systems have not considered allocating privileges to users based on (i) the relationship of the user with the device, (ii) the location and risk of the device and (iii) the current environment. In this work, we take a multi perspective view on the problem of sharing fine-grained privileges of IoT devices among multiple users in a smart home. We propose the concepts of user role (subset of privileges specific to each device), tasks and security levels (labels for each privilege) to allot right privileges to users. Thereby, limiting the exploitation of privileges assigned to legitimate insiders of the house. Thus, our work matches the aspirations of previous surveys on building a comprehensive access control system to manage privileges in a shared smart home.

Conformance checking of treatment plans in discharge summary data would facilitate the development of clinical decision support system, treatment plan quality assurance, and new treatment plan discovery. Conformance checking requires extraction of medical entities and relationships among them to form a computable representation of the treatment plan present in the discharge summary. We propose a workflow representation of patient’s discharge summary that is referred to as workflow instance. We employ a multi-layer perceptron neural network to extract relationships between medical entities to construct the workflow instance. The aim of this work is to check the conformance of the workflow instance against standard treatment plan. Standard treatment plans are extracted from the treatment guidelines provided on healthcare websites such as WebMD, Mayo Clinic, and Johns Hopkins. For each disease, these guidelines are curated, aggregated, and represented as a workflow specification. We commend multiple measures to compute the conformance of workflow instance with workflow specification. We validate our conformance checking methodology using discharge summary data of three diseases, namely colon cancer, coronary artery disease, and brain tumor, collected from THYME corpus and MIMIC III clinical database. Our approach and the solution can be used by hospitals and patients to determine adherence, gaps, and additions to standard treatment plans. Further, our work can facilitate to identify common errors and goodness in actual enactment of treatment plans, which can further lead to refinement of standard treatment plans.

In this work, we consider a multi-wheeled payload transport system. Each of the wheels can be selectively actuated. When they are not actuated, wheels are free moving and do not consume battery power. The payload transport system is modeled as an actuated multi-agent system, with each wheel-motor pair as an agent. Kinematic and dynamic models are developed to ensure that the payload transport system moves as desired. We design optimization formulations to decide on the number of wheels to be active and which of the wheels to be active so that the battery is conserved and the wear on the motors is reduced. Our multi-level control framework over the agents ensures that near-optimal number of agents is active for the payload transport system to function. Through simulation studies we show that our solution ensures energy efficient operation and increases the distance traveled by the payload transport system, for the same battery power. We have built the payload transport system and provide results for preliminary experimental validation.

In IoT applications, sensors produce raw data, and smart solutions consume the processed data. IoT driven smart solutions typically use data that is aggregated, filtered and processed to drive the smart solution. Such aggregated data has different semantics in comparison to raw sensor data, and it needs to be customized simultaneously for different application requirements. There is a need for proper data logistics for the movement of data from the sensors producing raw data to smart solutions consuming solution relevant data. In this paper, we present a data logistics system that includes (i) architecture to support the movement of data from sensors to smart applications, and (ii) event-driven solution to trigger workflows of smart solution. Workflows in our solution supports the data fulfillment requirements for the enactment of smart solutions.

Always-on agents are those agents which are like daemon programs that are always on and can do tasks as and when they arrive. These agents are idle when there is no task assigned to them. Further, those agents that work on a task, also wait for some event or task completion, and hence, are also idle for a short duration between execution of the tasks. The question arises what should the agent be doing when it is idle. In this paper, we conduct an empirical analysis to show improved decision-making capabilities by agents when they exploit their idle-time to analyze their past tasks in terms of decisions taken and their impact. The improvement of performance can be measured by considering an increase in success rate, avoiding strategies that may not work, quicker decision making, etc. Our always-on agent, while executing a task, stores some of the pertinent details of the task done in a database, such as decisions taken, paths of execution of the task, goodness of them, etc. The agent uses different strategies to use this stored knowledge. We present and evaluate three strategies that always-on agents can use (i) Frequent Decision Strategy (FDS) - the agent stores the prior executions and their frequency of success and failure, repeats the most frequent successful decision taken during prior executions of the task, (ii) Analyzed Decision Strategy (ADS) - the agent analyzes prior executions that were successful or not, stores in database the goodness of various alternatives and chooses the best alternative and (iii) Online analysis decision strategy (OADS) - the always-on agent while executing its task, during its idle time analyzes the possible future situations and prepares the list of best possible decisions that can be taken in future. Note that the FDS and ADS are used when the agent is not doing any task and is off-line, whereas, OADS generates new mock tasks to consider possible alternative task execution situations to expand its decision-making scenarios while having a task at hand. We conduct our empirical study on always-on agents playing connect-4 games to check the viability and show improved decision making.

In the game of hide and seek, one or more agents (hiders) can hide behind objects to prevent other agents (seekers) from seeing them through their line of sight (visibility). These hider agents can be eventually found by seekers who explore the environment. The hide and seek model serves as an abstraction of many real world scenarios such as police agents trying to capture intruders hiding in a warehouse, security/patrolling agents on the lookout for any suspicious activity, spying drones trying to prevent themselves from being caught. Medusa allows modeling such scenarios by supporting the simulation, visualization and analysis of hider and seeker agent strategies under different environments.

E-contract evolves over a period of time due to changes in e-contract environment. E-contract evolution adversely affects the execution of e-contracts. An e-contract is specified by a model at conceptual level, supported by a database management system (DBMS) at logical level and by both DBMS and workflow management system (WFMS) at implementation level. Any changes in the design as well as runtime environment during e-contract enactment must be reflected at all levels. Conventional modeling approaches simply model the e-contracts as specified workflows and execute them. Since, e-contracts are complex in nature, such models have to undergo large number of transformations during e-contract enactment. Metamodeling approach guides the correctness of transformed models by generating appropriate model instances according to e-contract constraints to support evolution. A metamodel has structural artifacts to capture the relationships among contract elements and model the required specifications and semantics present in an e-contract as a template. In this paper, we develop 1) an active metamodeling approach by a) introducing the taxonomy of evolution operations and b) handling metaevents to facilitate the structural and behavioral conformance during e-contracts evolution, and 2) an $({\rm ER}^{\ast{\rm EC}})$ architecture for enacting evolving e-contracts. Our methodology actively capture behavior features from e-contract executions to drive e-contract evolution.

Robots depend on captured images for perceiving the environment. A robot can replace a human in capturing quality photographs for publishing. In this paper, we employ an iterative photo capture by robots (by repositioning itself) to capture good quality photographs. Our image quality assessment approach is based on few high level features of the image combined with some of the aesthetic guidelines of professional photography. Our system can also be used in web image search applications to rank images. We test our quality assessment approach on a large and diversified dataset and our system is able to achieve a classification accuracy of 79%. We assess the aesthetic error in the captured image and estimate the change required in orientation of the robot to retake an aesthetically better photograph. Our experiments are conducted on NAO robot with no stereo vision. The results demonstrate that our system can be used to capture professional photographs which are in accord with the human professional photography.

The primary goal of the work in this paper is to present a novel visual user interface design that can be used by even novice users without much effort. The design of the user interface needs to ensure that the users require short learning curve. A compact WHEEL structure was chosen as it not only requires less screen space, but also, enables users to view and explore the data of large datasets (having many relationships) with minimal clicks and scrolling. The paper describes the design choices, design process, the final design and the results from the user tests which were conducted to evaluate the usability and utility of the interface.

The exponential number of possible subgraphs makes the problem of frequent subgraph mining a challenge. The set of maximal frequent subgraphs is much smaller to that of the set of frequent subgraphs providing ample scope for pruning. MARGIN is a maximal subgraph mining algorithm that moves among promising nodes of the search space along the “border” of the infrequent and frequent subgraphs. This drastically reduces the number of candidate patterns in the search space. The proof of correctness of the algorithm is presented. Experimental results validate the efficiency and utility of the technique proposed.

Modeling and deployment of e-contracts is a challenging task because of the involvement of both technological and business aspects. There are several frameworks and systems available in the literature. Some works mainly deal with the automatic handling of paper contracts and others provide monitoring and enactment of contracts. Because contracts evolve, it is useful to have a system that models and enacts the evolution of e-contracts.

The exponential number of possible subgraphs makes the problem of frequent subgraph mining a challenge. The set of maximal frequent subgraphs is much smaller to that of the set of frequent subgraphs, thus providing ample scope for pruning. MARGIN is a maximal subgraph mining algorithm that moves among promising nodes of the search space along the "border" of the infrequent and frequent subgraphs. This drastically reduces the number of candidate patterns considered in the search space. Experimental results validate the efficiency and utility of the technique proposed.

In this paper, we use a simple concept based on k-reverse nearest neighbor digraphs, to develop a framework RECORD for clustering and outlier detection. We developed three algorithms - (i) RECORD algorithm (requires one parameter), (ii) Agglomerative RECORD algorithm (no parameters required) and (iii) Stability-based RECORD algorithm (no parameters required). Our experimental results with published datasets, synthetic and real-life datasets show that RECORD not only handles noisy data, but also identifies the relevant clusters. Our results are as good as (if not better than) the results got from other algorithms.

In the business world, the conformance of respective business processes plays a significant role in the collaboration of business partners. Today, e-commerce technologies such as BPELAWS and ebXML have enabled enterprises to collaborate at the business process level. But there is little work on the study of conformance assessment of business processes in them. We extend the ebXML framework to support the conformance assessment of business processes. In this framework, a conformance assessment agent is designed to respond demands from a client enterprise, search ebXML registries for the business information of potential partners, test their conformance against the client's business process based on the well-defined conformance criteria, and submit an evaluation report to the client as a result. Via its help, appropriate business partners can be discovered for the client organization. Furthermore, we presents E-ADOME, a reference model of advanced workflow management systems, as the carrier of the conformance assessment agent.

Vertical partitioning is a design technique for reducing the number of disk accesses to execute a given set of queries by minimizing the number of irrelevant instance variables accessed. This is accomplished by grouping the frequently accessed instance variables as vertical class fragments. The complexity of object-oriented database models due to subclass hierarchy and class composition hierarchy complicates the definition and representation of vertical partitioning of the classes, which makes the problem of vertical partitioning in OODBs very challenging. In this paper, we develop a comprehensive analytical cost model for processing of queries on vertically partitioned OODB classes. A set of analytical evaluation results is presented to show the effect of vertical partitioning, and to study the trade-off between the projection ratio versus selectivity factor vis-a-vis sequential versus index access. Furthermore, an empirical experimental prototype supporting vertical class partitioning has been implemented on a commercial OODB tool kit to validate our analytical cost model.

E-services refer to services provided via the Internet. To provide value-added and composite e-services, we need an information system that can coordinate API-based, interactive web-based and human agents, internal and external to an organization. We present an environment to model and enact composite e-services as workflows, by extending our ADOME-WFMS (Advanced Object Modeling Environment-Workflow Management System) to E-ADOME, which supports composing new e-services and effective management of agents for enacting e-service.

Workflow is automation of a business process. A Workflow Management Systems (WFMS) is a system that assists in defining, managing and executing workflows. To support flexible enactment and adapive features, such as on-line workflow evolutionand exception handling, a WFMS requires advanced modeling functionality. As workflow evolution requires the modification of workflow definitions or adding ECA rules to the system during work in progress, an advanced schema evolution capability is required at run-time. It should be noted that the resolutions based on schema evolution are general-purpose ones, which can help reduce the occurrence of additional exceptions. As such, we have developed ADOME-WFMS based on Advanced Object Modeling Evnironment (ADMOE [4] - - an active OODBMS with role and synamic schema suppport), with a novel exception centric apporach. The contribution and objectives of our research with respect to workflow evolution are as follows.

Article On efficient storage space distribution among materialized views and indices in data warehousing environments Share on Authors: Ladjel Bellatreche University of Science & Technology, Clear Water Bay, Kowloon, Hong Kong University of Science & Technology, Clear Water Bay, Kowloon, Hong KongView Profile , Kamalakar Karlapalem University of Science & Technology, Clear Water Bay, Kowloon, Hong Kong University of Science & Technology, Clear Water Bay, Kowloon, Hong KongView Profile , Michel Schneider Université Blaise Pascal, 63177 Aubière Cédex, France Université Blaise Pascal, 63177 Aubière Cédex, FranceView Profile Authors Info & Claims CIKM '00: Proceedings of the ninth international conference on Information and knowledge managementNovember 2000 Pages 397–404https://doi.org/10.1145/354756.354846Online:06 November 2000Publication History 8citation642DownloadsMetricsTotal Citations8Total Downloads642Last 12 Months5Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

In this paper, we introduce structural join index hierarchy (SJIH) mechanisms that mimic the class composition hierarchy of the complex objects to provide direct access to complex objects and/or their component objects.SJIH extends the work on join index hierarchies by providing efficient and flexible access to complex objects, thus unifying various previous indexing methods proposed for object oriented database (OODB).A cost model for processing the queries and maintaining the indices is developed.A heuristic algorithm is designed to select appropriate indices to efficiently process a given set of queries.We show that SJIH indexing mechanisms facilitate efficient retrieval of complex objects.Given a set of queries and a limited index storage space, the heuristic algorithm facilitates fast selection of near-optimal set of indices for efficiently executing the queries.

Efficient query processing is a critical requirement for data warehousing systems as decision support applications often require minimum response times to answer complex, ad-hoc queries having aggregations, multi-ways joins over vast repositories of data. This can be achieved by fragmenting warehouse data. The data fragmentation concept in the context of distributed databases aims to reduce query execution time and facilitates the parallel execution of queries. In this paper, we propose a methodology for applying the fragmentation technique in a data warehouse star schema to reduce the total query execution cost. We present an algorithm for fragmenting the tables of a star schema. During the fragmentation process, we observe that the choice of the dimension tables used in fragmenting the fact table plays an important role on overall performance. Therefore, we develop a greedy algorithm in selecting "best" dimension tables. We propose an analytical cost model for executing a set of OLAP queries on a fragmented star schema. Finally, we conduct some experiments to evaluate the utility of the fragmentation for efficiently executing OLAP queries.

Exception handling in workflow management systems (WFMSs) is a very important problem since it is not possible to specify all possible outcomes and alternatives. On the other hand, cooperative support for user-driven computer supported resolution of unexpected exceptions and workflow evolution at run-time is vital for an adaptive WFMS. We have been developing ADOME-WFMS as a comprehensive framework in which the problem of workflow exception handling can be adequately addressed. We present an adaptive exception manager and its Web-based interface for ADOME-WFMS with procedures for supporting the following: effective management of problem solving agents, cooperative exception handling, user-driven computer supported resolution of unexpected exceptions, and workflow evolution.

Workflow systems are becoming very popular and are being used to support many of the day to day activities in large organizations. One of the major problems with workflow systems is that they often use heterogeneous and distributed hardware and software systems to execute a given activity. This gives rise to decentralized security policies and mechanisms, which, in order to enable activity execution, give too many privileges (for accessing resources like documents) to the agents (humans or systems) for executing the work. We develop the concept of least priviledge, wherein the set of agents are given just enough privileges to complete the given activities. We develop our concepts in the context of CapBasED-AMS (Capability-based and Event-driven Activity Management System). The CapBasED-AMS deals with the management and execution of activities. An activity consists of multiple inter-dependent tasks (atomic activities, each executed by a single agent) that need to be coordinated, scheduled and executed by a set of agents. We formalize the concept of least privilege security and present algorithms to statically assign least privilege assignment to the agents. Further, we develop the concept of dynamic least privilege enforcement, wherein an agent is given its privileges only during the duration of the task for which those privileges were assigned. We also develop the concept of dynamic evolution of least privileges by taking into consideration the changes in the way resources are accessed by the agents in executing their tasks. Finally, we address the trade-off between resilience to agent failure and least privilege.

A practitioner’s approach to integrate databases and evolve them so as to support new database applications is presented. The approach consists of a joint bottom-up and top-down methodology; the bottom-up approach is taken to integrate existing database using standard schema integration techniques (B-Schema), the top-down approach is used to develop a database schema for the new applications (T-Schema). The T-Schema uses a joint functional-data analysis. The B-schema is evolved by comparing it with the generated T-schema. This facilitates an evolutionary approach to integrate existing databases to support new applications as and when needed. The mutual completeness check of the T-Schema against B-Schema derive the schema modification steps to be performed on B-Schema to meet the requirements of the new database applications. A case study is presented to illustrate the methodology.Request access from your librarian to read this article's full text.

Distributed information processing, in many WWW applications, requires access to and the transfer and synchronization of large multimedia data objects (MDOs) across the communication network. Moreover, end users expect very fast response times and high QoS. Since the transfer of large MDOs across the communication network contributes to the response time observed by the end users, the problem of allocating these MDOs so as to minimize the response time is challenging. This problem becomes more complex in the context of hypermedia documents, in which the MDOs need to be synchronized during presentation to the end users. The basic problem of data allocation in distributed database environments is NP-complete. Therefore, there is a need to pursue and evaluate solutions based on heuristics which generate near-optimal MDO allocation. We address this problem by: (1) conceptualizing this problem by using a navigational model to represent hypermedia documents and their access behavior by end users, and by capturing the synchronization requirements on MDOs, (2) formulating the problem by developing a base case cost model for response time and generalizing it to incorporate user interaction and buffer memory constraints, (3) designing two algorithms to find near-optimal solutions for allocating MDOs of the hypermedia documents while adhering to the synchronization requirements, and (4) evaluating the trade-off between the time complexity to obtain the solution and the solution quality by comparing the solutions generated by the algorithms with the optimal solutions generated through an exhaustive search.

In a distributed deductive database system, queries which invoke rules executing at different sites and access different data need to be executed rather efficiently. Therefore, the rules invoked and relations accessed by the queries need to be allocated to sites so as to reduce the data transfer cost in processing a given set of queries. The rules and data allocation problem needs to take into consideration complex interdependencies among queries, rules and data. In this paper, we develop a comprehensive cost model for total data transfer incurred in processing a given set of queries by incorporating the dependencies among queries, rules and data. Furthermore, we develop an iterative approach to generate near-optimal solution for the combined rules and data allocation problem by using our cost model. In this approach, we start with an initial data allocation which is used for rule allocation, which in turn is used for data allocation, and so on. We stop this iterative rules and data allocation procedure when there is no further reduction in total data transfer cost incurred in processing the given set of queries. We also present the results of experiments conducted to evaluate the effectiveness of our approach by comparing the results with the exhaustive enumeration solution (which guarantees the optimal solution).

Many World Wide Web applications require access to and the transfer and synchronization of large multimedia data objects (MDOs), such as audio, video and images, across the communication network. The transfer of large MDOs contributes to the response time observed by the end users. As the end users expect strict adherence to response time constraints, the problem of allocating these MDOs so as to minimize response time becomes very challenging. The problem becomes more complex in the context of hypermedia documents (Web pages), wherein these MDOs need to be synchronized during presentation to the end users. Since the basic problem of data allocation in distributed database systems is NP-complete, a need exists to pursue and evaluate solutions based on heuristics for generating near-optimal MDO allocations. In this paper, we (i) conceptualize this problem by using a navigational model to represent hypermedia documents and their access behavior by end users, (ii) formulate the problem by developing a base case cost model for response time, (iii) design two algorithms to find near-optimal solutions for allocating MDOs of the hypermedia documents while adhering to the synchronization requirements, and (iv) evaluate the trade-off between the time complexity to obtain the solution and the quality of the solution by comparing the algorithms' solutions with an exhaustive solution over a set of experiments.

Workflow systems are becoming very popular and are being used to support many of the day to day activities in large organizations. One of the major problems with workflow systems is that they often use heterogeneous and distributed hardware and software systems to execute a given activity. This gives rise to decentralized security policies and mechanisms, which, in order to enable activity execution, give too many privileges to agents (humans or systems) for executing the work. We develop the concept of least privilege, wherein the set of agents are given just enough privileges to complete the given activities. We develop our concepts in the context of CapBasED-AMS (Capability-based and Event-driven Activity Management System). CapBasED-AMS deals with the management and execution of activities. An activity consists of multiple inter-dependent tasks (atomic activities, each executed by a single agent) that need to be coordinated, scheduled and executed by a set of agents. We formalize the concept of least privilege and present algorithms to statically assign least privilege assignment to the agents. We develop the concept of dynamic least privilege enforcement, wherein an agent is given its privileges only during the duration of the task for which those privileges were assigned. Finally, we introduce a metric, security risk factor and use it to evaluate the trade-off between least privilege and resilience to agent failure.

Data warehouses contain a vast amount of data, often from different sources. In order to support complex queries of various decision support systems, we need to store materialized views of data. These views represent integrated data based on complex aggregate queries, and should be available consistently and instantaneously, which would not be possible if those queries had to be invoked each time on the varied data sources. Conventional approaches have been based on the relational model where materialized views are stored as tables in the warehouse. This also means that semantics of the data warehouse structure are hidden. Using object-oriented methodology, we can explicitly represent the semantics and reuse view (class) definitions based on the ISA hierarchy and the class composition hierarchies, thereby resulting in a more efficient view mechanism. This paper deals with the issues concerned with providing an object-relational view (ORV) mechanism for the data warehouse. Primary issues are: providing object views for the relational data and for aggregate/summary data. These include translating the relational data structures into a class hierarchy, defining class structures for the summary views, supporting object ids for object instances of the views (classes) generated handling those classes with respect to maintenance and providing links to other classes in the hierarchy, and accessing and querying these view classes.

A major cost in executing queries in a distributed database system is the data transfer cost incurred in transferring relations (fragments) accessed by a query from different sites to the site where the query is initiated. The objective of a data allocation algorithm is to locate the fragments at different sites so as to minimize the total data transfer cost incurred in executing a set of queries. This is equivalent to minimizing the average query execution time, which is of primary importance in a wide class of distributed systems. The data allocation problem, however, is NP-complete, and thus requires fast heuristics to generate efficient solutions. The problem becomes more complex in the context of hypermedia documents (Web pages), within which the multimedia data objects (MDOs) need to be synchronized during presentation to the end users. Since the basic problem of data allocation in distributed database systems is NP-complete, we need heuristics which can generate near-optimal MDO allocations. In this paper, we propose a navigational model to represent hypermedia documents and their access behaviour from end users. We also formulate the problem by developing a base-case cost model for the response time, and we design an algorithm to find near-optimal solutions for allocating MDOs of the hypermedia documents while adhering to the synchronization requirements. We compare the algorithmic solution with an exhaustive solution over a set of experiments.

Summary form only given. The paper discusses exception handling in ADOME workflow management system (WMS). ADOME (Advanced Object Modeling Environment) is an active OODBMS extended with role facilities, built by integrating ITASCA (OODB) and CLIPS (inference engine). It provides a good supporting environment for the dynamic features required for a workflow management system, especially for online exception handling. In ADOME-WFMS, exception handlers can be procedural (extra branches of existing activity decomposition for exception handling) or declarative (in the form of ECA rules that can be specified within the scope of different activity and sub-activity levels).

The CapBasED-AMS (CAPability-BASed and Event-Driven Activity Management System) deals with the management and execution of activities. A problem-solving agent (PSA) is a human, a hardware system or a software system having the ability to execute activities. An activity consists of multiple interdependent tasks that need to be coordinated, scheduled and executed by a set of PSAs. Since security is an essential and integral part of activities, the activity management system has to manage and execute the activities in a secure way. In the CapBasED-AMS, threats such as unauthorized access or modification are identified as events. The security pilferage or illegal violation of privacy through the accessing of specification-time, compile-time or run-time data from the activity management system and the PSAs is monitored, controlled and reported. We present a secure CapBasED-AMS by taking into consideration: the system infrastructure; secure match-making with additional security constraints; security policies and a secure PSA; the task coordination model for security resource control from the PSA viewpoint, the organization viewpoint, the task viewpoint and the activity viewpoint by adapting a role-based resource security model; and secure execution of tasks with the PSA role-based security model.

Data warehouses are accessed by different queries with different frequencies. The portions of data accessed by a query can be treated as a view. When these views are related to each other and defined over overlapping portions of the base data, then it may be more efficient not to materialize all the views, but rather to materialize certain "shared views" from which the query results can be generated. We address some issues related to determining this set of shared views to be materialized in order to achieve the best combination of good performance and low maintenance, and provide an algorithm for achieving this goal.

The CapBasED-AMS (Capability-based and Event-driven Activity Management System) deals with the management and execution of activities. A Problem Solving Agent (PSA) is a human, or a hardware system, or a software system having an ability to execute activities. An activity consists of multiple inter-dependent tasks that need to be coordinated, scheduled and executed by a set of PSAs. Since security is essential and integral part of activities, the activity management system has to manage and execute the activities in a secure way. In the CapBasED-AMS, threats, such as, unauthorized access or modification are identified as events. In this paper, we develop a logical framework for security enforcement in CapBasED-AMS by taking into consideration the system infrastructure, secure match-making under security constraints, security policies and secure PSA, the task coordination model for security enforcement during activity execution.

Object-oriented database systems are becoming popular and are being used in a large number of application domain. Many of these application domains are inherently distributed. The focus of this work is on articulating the concepts of method induced partitioning schemes in object-oriented databases by understanding and classifying the object behavior embodied by the methods. We provide a solution for supporting fragmentation transparency by using method transformation. Finally, we present guidelines for method induced partitioning in object-oriented databases.

No abstract available.

A major cost in retrieving multimedia data from multiple sites is the cost incurred in transferring multimedia data objects (MDOs) from different sites to the site where the query is initiated. The objective of a data allocation algorithm is to locate the MDOs at different sites so as to minimize the total data transfer cost incurred in executing a given set of queries. The optimal allocation of MDOs depends on the query execution strategy employed by a distributed multimedia system while the query execution strategy optimizes a query based on this allocation. We fix the query execution strategy and develop a site-independent MDO dependency graph representation to model the dependencies among the MDOs accessed by a query. Given the MDO dependency graphs as well as the set of multimedia database sites, data transfer costs between the sites, the allocation limit on the number of MDOs that can be allocated at a site, and the query execution frequencies from the sites, an allocation scheme is generated. We formulate the data allocation problem as an optimization problem. We solve this problem with a number of techniques that broadly belong to three classes: max-flow min-cut, state-space search, and graph partitioning heuristics. The max-flow min-cut technique formulates the data allocation problem as a network-flow problem, and uses a hill-climbing approach to try to find the optimal solution. For the state-space search approach, the problem is solved using a best-first search algorithm. The graph partitioning approach uses two clustering heuristics, the agglomerative clustering and divisive clustering. We evaluate and compare these approaches, and assess their cost-performance trade-offs. All algorithms are also compared with optimal solutions obtained through exhaustive search. Conclusions are also made on the suitability of these approaches to different scenarios.

Cooperative problem solving can be viewed as a complex activity requiring harmonious and dynamic interaction between active agents and passive agents. This problem is currently being addressed by the research community at various levels of abstraction. Broadly, this paper analyzes the problem of cooperative problem solving from a database perspective and argues that recent advances in database technology facilitate development of a viable solution to the above problem. Specifically, in this paper, we first analyze the problem of cooperative problem solving to identify its underlying key characteristics. Based upon our analysis, we partition the problem space into classes along a spectrum and indicate the level of cooperation required. We propose near-term as well as long-term solutions for cooperative problem solving that progressively enhances the functionality of database systems by synthesizing appropriate abstractions and techniques. Furthermore, we identify problems, such as agent capability modeling that require further research to address the most general form of cooperative problem solving.