1954441
A Sufficient and Necessary Condition for the Consistency of XML DTDs
Recently, XML has emerged as a standard for representing and exchanging data on the World Wide Web. As a result, there is a trend of increasing amount of XML documents that publish information on the Web from various data sources. A Document Type Definition (DTD) describes the structure of a set of similar XML documents and serves as the schema for XML documents. The World Wide Web Consortium has defined the grammar for specifying DTDs; however, even a syntactically correct DTD might be inconsistent in the sense that there exist no XML documents conforming to the structure imposed by the DTD. In this paper, we formalize the notion of the consistency of DTDs, and identify a sufficient and necessary condition for a DTD to be consistent.
18131628
Automatic workflow verification and generation
Correctness is an important aspect of workflow management systems. However, most of the workflow literature focuses only on the modeling aspects and assumes that a workflow is correct if during the execution it respects the control and data dependency specified by the workflow designer. To address the correctness question properly we propose a new workflow model based on Hoare semantics that allows to: (1) automatically check if the desired outcome of a workflow can be produced by its actual implementation, (2) automatically synthesize a workflow implementation from the workflow specification and a given task library.In particular we: (1) formalize the semantics of workflows and tasks with pre-and postconditions, (2) for each control construct we provide a set of sound inference rules formalizing its semantics. While most of our workflow constructs are standard, two of them are new: the universal and the existential constructs. We then describe algorithms for automatically checking the correctness of workflows and for automatic workflow generation.
6486168
A Dataflow-Oriented Atomicity and Provenance System for Pipelined Scientific Workflows
Scientific workflows have gained great momentum in recent years due to their critical roles in e-Science and cyberinfrastructure applications. However, some tasks of a scientific workflow might fail during execution. A domain scientist might require a region of a scientific workflow to be "atomic". Data provenance, which determines the source data that are used to produce a data item, is also essential to scientific workflows. In this paper, we propose: (i) an architecture for scientific workflow management systems that supports both provenance and atomicity; (ii) a dataflow-oriented atomicity model that supports the notions of commit and abort; and (iii) a dataflow-oriented provenance model that, in addition to supporting existing provenance graphs and queries, also supports queries related to atomicity and failure.
7682350
Incremental genetic K-means algorithm and its application in gene expression data analysis
BackgroundIn recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data.ResultsIn this paper, we propose a new clustering algorithm, Incremental Genetic K-means Algorithm (IGKA). IGKA is an extension to our previously proposed clustering algorithm, the Fast Genetic K-means Algorithm (FGKA). IGKA outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value Total Within-Cluster Variation (TWCV) and to cluster centroids incrementally whenever the mutation probability is small. IGKA inherits the salient feature of FGKA of always converging to the global optimum. C program is freely available at http://database.cs.wayne.edu/proj/FGKA/index.htm.ConclusionsOur experiments indicate that, while the IGKA algorithm has a convergence pattern similar to FGKA, it has a better time performance when the mutation probability decreases to some point. Finally, we used IGKA to cluster a yeast dataset and found that it increased the enrichment of genes of similar function within the cluster.
1137858
Runtime Security Verification for Itinerary-Driven Mobile Agents
We present a new approach to ensure the secure execution of itinerary-driven mobile agents, in which the specification of the navigational behavior of an agent is separated from the specification of its computational behavior. We empower each host with an access control policy so that the host will deny the access from an agent whose itinerary does not conform to the host's access control policy. A host uses model checking algorithms to check if the itinerary of the agent conforms to its access control policy written in mu-calculus, and if so, grant access permission. In order to address the state explosion problem for model checking itineraries, we propose an approach called model generation code. In this approach, instead of verifying the itinerary itself, a host actually checks the conservative models of a mobile agent. If a conservative model does not satisfy the host's access control policy, the mobile agent will provide refined models for further verification. Our preliminary results show that this is a practical and promising approach to ensure the secure execution of mobile agents
14239575
Semantics based verification and synthesis of BPEL4WS abstract processes
We introduce a logic model to formally specify the semantics of workflows and their composite tasks described as BPEL4WS abstract processes. Based on the model, we present a set of inference rules to deduce the strongest postcondition and weakest precondition of a workflow and demonstrate that automatic workflow verification is possible due to the restrictions on data manipulation in an abstract process. We then sketch an algorithm that automatically synthesizes a workflow given its specification and a task library.
44527180
A Robust Data Delivery Protocol for Large Scale Sensor Networks
Recent technology advances in low-cost, low-power chip designs have made feasible the deployment of large-scale sensor networks. Although data forwarding has been among the first set of issues explored in sensor networking, how to reliably deliver sensing data through a vast field of small, vulnerable sensors remains a research challenge. In this paper we present GRAdient Broadcast (GRAB), a new set of mechanisms and protocols which is designed specifically for robust data delivery in spite of unreliable nodes and fallible wireless links. Similar to previous work [1], GRAB builds and maintains a cost field, providing each sensor in the network the direction to forward sensing data. Different from all the existing approaches, however, GRAB forwards data along an interleaved mesh from each source to the receiver. The width of the forwarding mesh is controlled by the amount of credit carried in each data message, allowing the degree of delivery robustness to be adjusted by the sender. GRAB design harnesses the advantage of large scale and relies on the collective efforts of multiple nodes to deliver data, without dependency on any individual ones. As demonstrated in our extensive simulation experiments, GRAB can successfully deliver above 90% of data with relatively low energy cost even under adverse conditions of up to 30% node failures compounded with 15% link packet losses.
896953
Sequential Aggregate Signatures and Multisignatures Without Random Oracles
We present the first aggregate signature, the first multisignature, and the first verifiably encrypted signature provably secure without random oracles. Our constructions derive from a novel application of a recent signature scheme due to Waters. Signatures in our aggregate signature scheme are sequentially constructed, but knowledge of the order in which messages were signed is not necessary for verification. The aggregate signatures obtained are shorter than Lysyanskaya et al. sequential aggregates and can be verified more efficiently than Boneh et al. aggregates. We also consider applications to secure routing and proxy signatures.
611972
Tracking Causal Dependencies in an Active Object-Oriented Database
As active and deductive databases are applied to more complex domains (e.g. engineering design), the need to track the causality of data which has been derived by rules in the database becomes vital. Truth maintenance systems have been used in the area of artificial intelligence to track the causal dependencies of data derived from rules in knowledge-based systems. These same techniques can be applied to active object-oriented databases to provide the users with the means to track the relationships between data and rules. This paper describes the relevant features of a simple truth maintenance systems and puts forth a model of how truth maintenance systems can be integrated with an active object-oriented database, including the definition of new query operators to interpret the causal information for the end user.
24216233
CAQ: A machine learning tool for engineering
Abstract Machine learning algorithms developed from artificial intelligence (AI) research are rapidly finding useful applications in engineering domains. They are joining a family of tools that can help engineers summarize (or synthesize) massive amounts of data to support decision making. Traditional tools in this family, such as various statistical-based techniques, deal mostly with continuous (real) valued attributes — their results are relatively accurate, but often hard to interpret. On the other hand, logic-based learning tools, such as Michalski's AQ series of programs for learning concepts from examples, deal only with discrete valued attributes — their results are easier to comprehend, but not accurate for engineering applications which rarely deal solely with discrete valued attributes. Ideally, engineers need algorithms that work with a mix of attributes including those that take continuous values and those that take discrete values. Engineering problems also demand that learning algorithms be able to handle noise and produce concepts that are comprehensible. CAQ (Continuous AQ) is an algorithm designed to meet engineering requirements. This paper explains how CAQ combines continuous and discrete values, and shows that handling continuous attributes as real numbers instead of forcing them into a discrete representation leads to more efficient concept formation. Data from a turning process simulator used in support of machine operation planning in manufacturing are used to demonstrate the new algorithm.
35964038
Learning Engineering Models with the Minimum Description Length Principle
This paper discusses discovery of mathematical models from engineering data sets. KEDS, a Knowledge-based Equation Discovery System, identifies several potentially overlapping regions in the problem space, each associated with an equation of different complexity and accuracy. The minimum description length principle, together with the KEDS algorithm, is used to guide the partitioning of the problem space. The KEDSMDL algorithm has been tested on discovering models for predicting the performance efficiencies of an internal combustion engine.