Labeling recursive workflow executions on-the-fly

Abstract

This paper presents a compact labeling scheme for answering reachability queries over workflow executions. In contrast to previous work, our scheme allows nodes (processes and data) in the execution graph to be labeled on-the-fly, i.e., in a dynamic fashion. In this way, reachability queries can be answered as soon as the relevant data is produced. We first show that, in general, for workflows that contain recursion, dynamic labeling of executions requires long (linear-size) labels. Fortunately, most real-life scientific workflows are linear recursive, and for this natural class we show that dynamic, yet compact (logarithmic-size) labeling is possible. Moreover, our scheme labels the executions in linear time, and answers any reachability query in constant time. We also show that linear recursive workflows are, in some sense, the largest class of workflows that allow compact, dynamic labeling schemes. Interestingly, the empirical evaluation, performed over both real and synthetic workflows, shows that our proposed dynamic scheme outperforms the state-of-the-art static scheme for large executions, and creates labels that are shorter by a factor of almost 3. ABSTRACTThis paper presents a compact labeling scheme for answering reachability queries over workflow executions. In contrast to previous work, our scheme allows nodes (processes and data) in the execution graph to be labeled on-the-fly, i.e., in a dynamic fashion. In this way, reachability queries can be answered as soon as the relevant data is produced. We first show that, in general, for workflows that contain recursion, dynamic labeling of executions requires long (linearsize) labels. Fortunately, most real-life scientific workflows are linear recursive, and for this natural class we show that dynamic, yet compact (logarithmic-size) labeling is possible. Moreover, our scheme labels the executions in linear time, and answers any reachability query in constant time.We also show that linear recursive workflows are, in some sense, the largest class of workflows that allow compact, dynamic labeling schemes. Interestingly, the empirical evaluation, performed over both real and synthetic workflows, shows that our proposed dynamic scheme outperforms the state-of-the-art static scheme for large executions, and creates labels that are shorter by a factor of almost 3.

Cleaning structured event logs: A graph repair approach

“…Operator Successor 3 proof J. Zhe X. Feng t 4 -O. Chu X. Feng t 5 evaluate X. Feng System2 t 6 archive System2 - 6 steps (events t 1 to t 6 ) of accomplishing a part design, a.k.a., an execution trace. Each event includes a Name of being processed task, the Operator executing the task, and the Successors of the follow-up activities being assigned to.…”

“…The links of Successor and Operator between events indicate the structural information. For example, the Successor of t 2 assigns the follow-up tasks to J.Zhe and O. Chu (corresponding to Operators in t 3 and t 4 , respectively). It indicates the links from t 2 to t 3 and t 4 as illustrated in the graph of Figure 1(a).…”

“…Due to various event recording conventions, a simple proof event name is reported in t 3 . It is not clear whether it denotes insulation proof, electrician proof or proof check.…”

“…Additional conditions may be added in the query, such as type information [12] or probability [14]. Moreover, as an important application, provenance queries on workflows are well investigated [23], [1], [2], [3]. A typical provenance query calculates the transitive closure of dependencies of an event in the process data.…”

Answering regular path queries on workflow provenance

“…Most relevant for this paper are the dynamic reachability labeling techniques of [3], [4], [5] for workflow provenance graphs, which address reachability queries between a single pair of nodes. In contrast, this paper addresses considerably more complex queries, regular path queries, between sets of nodes.…”