14016627
Semantic conditions for correctness at different isolation levels
Many transaction processing applications execute at isolation levels lower than serializable in order to increase throughput and reduce response time. The problem is that non-serializable schedules are not guaranteed to be correct for all applications. The semantics of a particular application determines whether that application will run correctly at a lower isolation level, and in practice it appears that many applications do. Unfortunately, we know of an analysis technique that has been developed to test an application for its correctness at a particular level. Apparently decisions of this nature are made on an informal basis. In this paper we describe such a technique in a formal way. We use a new definition of correctness, semantic correctness, which is weaker than serializability, to investigate the correctness of such executions. For each isolation level, we prove a condition under which transactions that execute at that level will be semantically correct. In addition to the ANSI/ISO isolation levels of read uncommitted, read committed, and repeatable read, we also prove a condition for correct execution at the read committed with first-committer-wins (a variation of read committed) and at the snapshot isolation level. We assume that different transactions can be executing at different isolation levels, but that each transaction is executing at least at the read uncommitted level.
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.
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.
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.
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.
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.
527997
Statistical en-route filtering of injected false data in sensor networks
In a large-scale sensor network individual sensors are subject to security compromises. A compromised node can inject into the network large quantities of bogus sensing reports which, if undetected, would be forwarded to the data collection point (i.e. the sink). Such attacks by compromised sensors can cause not only false alarms but also the depletion of the finite amount of energy in a battery powered network. We present a statistical en-route filtering (SEF) mechanism that can detect and drop such false reports. SEF requires that each sensing report be validated by multiple keyed message authentication codes (MACs), each generated by a node that detects the same event. As the report is forwarded, each node along the way verifies the correctness of the MACs probabilistically and drops those with invalid MACs at earliest points. The sink further filters out remaining false reports that escape the en-route filtering. SEF exploits the network scale to determine the truthfulness of each report through collective decision-making by multiple detecting nodes and collective false-report-detection by multiple forwarding nodes. Our analysis and simulations show that, with an overhead of 14 bytes per report, SEF is able to drop 80/spl sim/90% injected false reports by a compromised node within 10 forwarding hops, and reduce energy consumption by 50% or more in many cases.
17835959
Toward resilient security in wireless sensor networks
Node compromise poses severe security threats in wireless sensor networks. Unfortunately, existing security designs can address only a small, fixed threshold number of compromised nodes; the security protection completely breaks down when the threshold is exceeded. In this paper, we seek to overcome the threshold limitation and achieve resiliency against an increasing number of compromised nodes. To this end, we propose a novel location-based approach in which the secret keys are bound to geographic locations, and each node stores a few keys based on its own location. The location-binding property constrains the scope for which individual keys can be (mis)used, thus limiting the damages caused by a collection of compromised nodes. We illustrate this approach through the problem of report fabrication attacks, in which the compromised nodes forge non-existent events. We evaluate our design through extensive analysis, implementation and simulations, and demonstrate its graceful performance degradation in the presence of an increasing number of compromised nodes.
1764244
Machine Learning in Engineering Automation
Abstract Engineers need intelligent tools to assist them with problems such as design, planning, monitoring, control, diagnosis, and analysis. Manual construction of these tools can be costly or impossible due to problems such as large amounts of data, lack of problem understanding, and the expense of knowledge engineering. Machine learning techniques hold promise for assisting in solutions to many of these problems, but engineering domains present significant challenges to learning systems, including: noisy data, continuous quantities, mathematical formulas, large problem spaces, incorporating multiple sources and forms of knowledge, and the need for user-system interaction. This paper describes a number of challenges to learning systems motivated by engineering applications and describes a taxonomy of engineering tasks for application of machine learning technology.
1875526
Design Issues of Prefetching Strategies for Heterogeneous Software DSM
In distributed shared memory (DSM) systems, one of the major overheads is page fault, because it induces remote memory access latencies. Prefetching strategy permits home nodes sending data to remote nodes in advance, in order to reduce page faults and latencies, as also to improve overall performance; however, it causes accumulated waiting phenomenon, waiting synchronization phenomenon, among others. In heterogeneous environments, considering the fact of different ability among computing hosts, it increases idle time and synchronization time. Therefore, the search for alternatives to reduce these overheads is important. In this paper, we propose a method that utilizes concepts of load balancing in prefetching strategy, where the goal is to reduce idle time and synchronization time. Basically, this approach distributes home pages to appropriate computing nodes and adjusts prefetching strategy's policies into heterogeneous environments
30892527
On Design and Implementation of Adaptive Data Classification Scheme for DSM Systems
Distributed Shared Memory (DSM) environment is built by using specific softwares, to combine a number of computer hardware resources into one computing environment. Such environment not only provides an easy way to execute parallel applications, but also combines resources to speedup execution of these applications. DSM systems need to maintain data consistency in memory, what usually leads to communication overhead. Therefore, there exist a number of strategies that can be used to overcome this overhead and improve overall performance. Prefetching strategies have been proven to show great performance in DSM systems, since they can reduce data access communication latencies from remote nodes. However, these strategies also transfer unnecessary prefetching pages to remote nodes. In this research paper, we focus on the analysis of data access pattern during execution of parallel applications. We propose an Adaptive Data Classification scheme to improve prefetching strategy, with the goal to improve overall performance. Adaptive Data Classification scheme classifies data according to the access behavior of pages, so that home node uses past history access patterns of remote nodes to decide whether it needs to transfer related pages to remote nodes. From experimental results, our method can improve the performance of prefetching strategies in DSM systems.