Labeling Workflow Views with Fine-Grained Dependencies

This paper considers the problem of efficiently answering reachability queries over views of provenance graphs, derived from executions of workflows that may include recursion. Such views include composite modules and model fine-grained dependencies between module inputs and outputs. A novel view-adaptive dynamic labeling scheme is developed for efficient query evaluation, in which view specifications are labeled statically (i.e. as they are created) and data items are labeled dynamically as they are produced during a workflow execution. Although the combination of fine-grained dependencies and recursive workflows entail, in general, long (linear-size) data labels, we show that for a large natural class of workflows and views, labels are compact (logarithmic-size) and reachability queries can be evaluated in constant time. Experimental results demonstrate the benefit of this approach over the state-of-the-art technique when applied for labeling multiple views.Comment: VLDB201

Answering Regular Path Queries on Workflow Provenance

This paper proposes a novel approach for efficiently evaluating regular path queries over provenance graphs of workflows that may include recursion. The approach assumes that an execution g of a workflow G is labeled with query-agnostic reachability labels using an existing technique. At query time, given g, G and a regular path query R, the approach decomposes R into a set of subqueries R1, ..., Rk that are safe for G. For each safe subquery Ri, G is rewritten so that, using the reachability labels of nodes in g, whether or not there is a path which matches Ri between two nodes can be decided in constant time. The results of each safe subquery are then composed, possibly with some small unsafe remainder, to produce an answer to R. The approach results in an algorithm that significantly reduces the number of subqueries k over existing techniques by increasing their size and complexity, and that evaluates each subquery in time bounded by its input and output size. Experimental results demonstrate the benefit of this approach

Decreased expression of dual-specificity phosphatase 9 is associated with poor prognosis in clear cell renal cell carcinoma

Background: The molecular mechanisms involved in the development and progression of clear cell renal cell carcinomas (ccRCCs) are poorly understood. The objective of this study was to analyze the expression of dual-specificity phosphatase 9 (DUSP-9) and determine its clinical significance in human ccRCCs. Methods: The expression of DUSP-9 mRNA was determined in 46 paired samples of ccRCCs and adjacent normal tissues by using real-time qPCR. The expression of the DUSP-9 was determined in 211 samples of ccRCCs and 107 paired samples of adjacent normal tissues by immunohistochemical analysis. Statistical analysis was performed to define the relationship between the expression of DUSP-9 and the clinical features of ccRCC. Results: The mRNA level of DUSP-9, which was determined by real-time RT-PCR, was found to be significantly lower in tumorous tissues than in the adjacent non-tumorous tissues (p < 0.001). An immunohistochemical analysis of 107 paired tissue specimens showed that the DUSP-9 expression was lower in tumorous tissues than in the adjacent non-tumorous tissues (p < 0.001). Moreover, there was a significant correlation between the DUSP-9 expression in ccRCCs and gender (p = 0.031), tumor size (p = 0.001), pathologic stage (p = 0.001), Fuhrman grade (p = 0.002), T stage (p = 0.001), N classification (p = 0.012), metastasis (p = 0.005), and recurrence (p < 0.001). Patients with lower DUSP-9 expression had shorter overall survival time than those with higher DUSP-9 expression (p < 0.001). Multivariate analysis indicated that low expression of the DUSP-9 was an independent predictor for poor survival of ccRCC patients. Conclusion: To our knowledge, this is the first study that determines the relationship between DUSP-9 expression and prognosis in ccRCC. We found that decreased expression of DUSP-9 is associated with poor prognosis in ccRCC. DUSP-9 may represent a novel and useful prognostic marker for ccRCC

Efficiently tracking provenance in scientific workflows

Tracking the provenance of data produced by a workflow execution involves answering reachability queries over large provenance graphs, which can be expensive. For that, we present compact labeling schemes for efficiently answering reachability queries over provenance graphs that are derived from executions of a given workflow specification. The idea is to assign each node a reachability label such that using only the labels of any two nodes, we can quickly decide if one can reach the other. Our proposed schemes build logarithmic-size labels in linear time, and answer any query in constant time. In this dissertation, we consider the reachability labeling problem for a variety of workflow settings. First, we study the static labeling problem, where the entire provenance graph is given as input. For workflows with well-nested loops and forks (i.e., parallel executions), we develop a skeleton-based labeling approach which uses the labeling for the specification as an effective skeleton for designing the labeling for its executions. Next, we turn to the dynamic labeling problem, where the input provenance graph grows over time but the nodes must be labeled on-the-fly. We first show that, in general, for workflows that contain arbitrary recursion, dynamic labeling of their executions requires long (linear-size) labels. Nevertheless, we identify a natural class of workflows with linear recursion, for which dynamic, yet compact (logarithmic-size) labeling is possible. Finally, we revisit the dynamic labeling problem when fine-grained dependencies between inputs and outputs of modules are defined over multiple workflow views. It turns out that the restriction of linear recursion, which suffices to reduce the label length before, is no longer helpful. However, for a more restricted class of workflows with strictly linear recursion and safe views, we propose a novel view-adaptive dynamic labeling approach