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.
41350051
Language engineering for the Semantic Web: a digital library for endangered languages
16579446
Mining Correlation between Motifs and Gene Expression
One of the major challenges in the post-genomic era is to determine all DNA-binding transcription factors (TFs) and their regulatory binding sites (motifs) within the genomes. To discover the relationship between the motifs and changes in gene expression, we propose a new algorithm, co-miner (correlation miner). Correlation rules are generated based on the expression profiles of genes with significant expression change through the time course of gene expression. Thus, we may consider the change in gene expression to be causatively associated with the transcription binding sites in the upstream sequences. In addition, we introduce partition and constraint pushing techniques to improve the performance and demonstrate their effectiveness by our experiments. By applying co-miner to a yeast dataset, the relationships between motifs and gene expression revealed by co-miner are confirmed in the literature.
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.
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.
172967
XML subtree reconstruction from relational storage of XML documents
Numerous researchers have proposed to use relational databases to store and query XML documents. In these systems, the elements selected by an XML query are returned to an application either by select mode or by reconstruct mode. For the reconstruct mode, the XML subtrees that are rooted at the selected elements need to be extracted and reconstructed from the relational storage of XML documents. Therefore, XML subtree reconstruction is an important problem since its efficiency has a significant impact on XML query response time. In this paper, we propose (i) a linear XML subtree reconstruction algorithm Reconstruct to reconstruct an XML subtree from the structure-encoded sequence of the subtree that is extracted from the relational database by a structure-encoded sequence retrieval algorithm, (ii) a generic efficient structure-encoded sequence retrieval algorithm RD-SB for a schema-based relational XML storage, and (iii) a generic efficient structure-encoded sequence retrieval algorithm RD-SL for a schema-less relational XML storage. To the best of our knowledge, our algorithms provide the first generic solutions to the XML subtree reconstruction problem that are applicable to all relational XML storage schemes proposed in the literature. Finally, our experiments show that our algorithms are efficient and scalable.
15553830
A distributed artificial intelligence approach to integrated engineering design
This paper reports the development of a computer-based integrated engineering design environment which facilitates design decision-making by providing a medium for increased communication and cooperation amongst product development participants. A refinement and constraint propagation model for product development is used within a distributed artificial intelligence (DAI) architecture to support the cooperative design effort. Within such an environment, product and process designers can integrate the recommendations and expertise of all product development concerns as early as possible within the design-cycle. Our prototype system, ENVIED (Environment for Integrated Engineering Design), provides a domain-independent framework for human designers, artificial intelligence systems, and traditional software systems to cooperatively perform product and process design.
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.
23906830
PEAS: a robust energy conserving protocol for long-lived sensor networks
Small, inexpensive sensors with limited memory, computing power and short battery lifetimes are turning into reality. Due to adverse conditions such as high noise levels, extreme humidity or temperatures, or even destructions from unfriendly entities, sensor node failures may become norms rather than exceptions in real environments. To be practical, sensor networks must last for much longer times than that of individual nodes, and have yet to be robust against potentially frequent node failures. This paper presents the design of PEAS, a simple protocol that can build a long-lived sensor network and maintain robust operations using large quantities of economical, short-lived sensor nodes. PEAS extends system functioning time by keeping only a necessary set of sensors working and putting the rest into sleep mode. Sleeping ones wake up now and then, probing the local environment and replacing failed ones. The sleeping periods are self-adjusted dynamically, so as to keep the sensors' wakeup rate roughly constant, thus adapting to high node densities.
11410811
Fair scheduling in wireless packet networks
Fair scheduling of delay and rate-sensitive packet flows over a wireless channel is not addressed effectively by most contemporary wireline fair-scheduling algorithms because of two unique characteristics of wireless media: (1) bursty channel errors and (2) location-dependent channel capacity and errors. Besides, in packet cellular networks, the base station typically performs the task of packet scheduling for both downlink and uplink flows in a cell; however, a base station has only a limited knowledge of the arrival processes of uplink flows. We propose a new model for wireless fair-scheduling based on an adaptation of fluid fair queueing (FFQ) to handle location-dependent error bursts. We describe an ideal wireless fair-scheduling algorithm which provides a packetized implementation of the fluid mode, while assuming full knowledge of the current channel conditions. For this algorithm, we derive the worst-case throughput and delay bounds. Finally, we describe a practical wireless scheduling algorithm which approximates the ideal algorithm. Through simulations, we show that the algorithm achieves the desirable properties identified in the wireless FFQ model.
13148267
Fair scheduling in wireless packet networks
Fair scheduling of delay and rate-sensitive packet flows over a wireless channel is not addressed effectively by most contemporary wireline fair scheduling algorithms because of two unique characteristics of wireless media: (a) bursty channel errors, and (b) location-dependent channel capacity and errors. Besides, in packet cellular networks, the base station typically performs the task of packet scheduling for both downlink and uplink flows in a cell; however a base station has only a limited knowledge of the arrival processes of uplink flows.In this paper, we propose a new model for wireless fair scheduling based on an adaptation of fluid fair queueing to handle location-dependent error bursts. We describe an ideal wireless fair scheduling algorithm which provides a packetized implementation of the fluid model while assuming full knowledge of the current channel conditions. For this algorithm, we derive the worst-case throughput and delay bounds. Finally, we describe a practical wireless scheduling algorithm which approximates the ideal algorithm. Through simulations, we show that the algorithm achieves the desirable properties identified in the wireless fluid fair queueing model.