Ikc 2015
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This talk presents techniques for improving fragment reuse in workflows.
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Ikc 2015
- 1. Mariem Harmassi, Daniela Grigori, Khalid Belhajjame LAMSADE, Université Paris Dauphine Mining Workflow Repositories for Improving Fragments Reuse
- 2. Workflows A business process specified using the BPMN notation A Scientific Workflow system (Taverna) A workflow consists of an orchestrated and repeatable pattern of business activity enabled by the systematic organization of resources into processes that transform materials, provide services, or process information (Workflow Coalition) IKC 20152
- 3. Scientific Workflows Scientific workflows are increasingly used by scientists as a means for specifying and enacting their experiments. They tend to be data intensive The data sets obtained as a result of their enactment can be stored in public repositories to be queried, analyzed and used to feed the execution of other workflows.IKC 20153
- 4. Workflows are difficult to design The design of scientific workflows, just like business process, can be a difficult task Deep knowledge of the domain Awareness of the resources, e.g., programs and web services, that can enact the steps of the workflow Publish and share workflows, and promote their reuse. myExperiment, CrowldLab, Galaxy, and other various business process repository Reuse is still an aim. There are no capabilities that support the user in identifying the workflows, or fragments thereof, that are relevant for the task at hand.IKC 20154
- 5. Fragment look-up in the life cycle of workflow design Design Workflow Search Fragments Run Workflow PublishWorkflow Workflow repositories IKC 20155
- 6. Workflow Fragments Search Why is it useful for? The workflow designer knows the steps of the fragment and their dependencies, but does not know the resources (programs or web services) that can be used for their implementation. The designer may want to know how colleagues and third parties designed the fragment (best practices) Elements of the solution 1. Filtering: Instead of search the whole repository, we limit the number of workflows in the repository to be examined to those that are relevant to the user 2. Identify the fragments that are reccurrent in the workflows retrieved in (1) IKC 20156
- 7. 1 - Filtering step Workflow XML Workflow graph List of keywords List of keywords & synonyms Wordnet BP Repository Filter Else IKC 20157
- 8. 2- Identify Recurrent Fragments We use graph mining algorithms to identify the fragments in the repository that are recurrent. We use the SUBDUE algorithm. Which graph representation to use to represent (workflow) fragments? We examined a number of workflow representation IKC 20158
- 10. Representation C att 2 att 3 att 4 att 5 att 1 S-att1-att2 S-att1-att3 seq-att2-att4 seq-att4-att5 att 2 att 3 att 5 att 1 S-att1-att2 S-att1-att3 seq-att3-att5 IKC 201510
- 11. att 1 att 2 att 3 att 4 att 5 And_att1_att3 And_att1_att2 XOR_att3_att5 SEQ_att2_att 4 XOR_att4_att5 Representation D Representation D1 att 1 att 2 att 3 att 4 att 5 An d And XOR SEQ XOR IKC 201511
- 12. Experiments 1st experiment: To assess the suitability of the graph representations for mining workflow graphs Effectiveness : Precision/ Recall Memory space : Disk space, DIV Execution time 2nd experiment: To assess the impact of the filtering step in narrowing the search to relevant workflow fragments. IKC 201512
- 13. Experiment 1: Dataset We created three datasets of workflow specifications, containing respectively 30, 42, and 71 workflows. 9 out of these workflows are similar to each other and, as uch contain recurrent structures, that should be detected by the mining algorithm. Despite the small size of the collection, these datasets allowed to distinguish to a certain extent between the different representations. IKC 201513
- 14. Experimentation1: Input Data size IKC 201514
- 18. Experiment1: Execution Time ≥ 55 times ≥ 25 times ≈ 4 times ≈ 5 times IKC 201518
- 19. Experiment1: Summary control nodes : recurrent patterns typical coding scheme related to the model rule Recall Labeling the edges: specializations of the same abstract workflow. Precision Xor as a set of alternatives: duplication , loss of informations Recall Precision The Representation D1 seems to be therefore the one that performs best IKC 201519
- 20. Experiment 2 Data sets: All Taverna 1 workflows (498 workflows) from myExperiment User query: We use a small fragment from a workflow in myExperiment. IKC 201520
- 21. Conclusion Methodology for improving the reusability Model of representation D + Filter Improve the filter Test others similarity measures Need to assess the usefulness of the technics presented in practice. And how they can be incorporated in the workflow design life cycle. In the context of the Contextual and Aggregrated Information Retrieval (CAIR) project IKC 201521
- 22. Mariem Harmassi, Daniela Grigori, Khalid Belhajjame LAMSADE, Université Paris Dauphine Mining Workflow Repositories for Improving Fragments Reuse
Editor's Notes
- Workflows are increasingly used by scientists as a means for specifying and enacting their experiments. Such workflows are often data intensive [5]. The data sets obtained by their enactment have several applications, e.g., they can be used to understand new phenomena or confirm known facts, and therefore such data sets are worth storing (or preserving) for future analyzes.
- -scientific workflows have been used to encode in-silico experiments. -The design of scientific workflows can be a difficult task . It requires a deep knowledge of the domain as well as awareness of the programs and services available for implementing the workflow steps. -In 2009, De Roure and coauthors pointed out the advantages of sharing and reusing workflows from scientific workflows repositores like MyExperiment, Crowdlabs, Galaxy and others. -The problem is that the size of these repositories is continuously growing and many problems relating to the reuse of available workflows emerged, example it become difficlut to distinguish a special use case from a usage pattern -So using mining techniques forms a goos solution. Lets discuss the most important contributions in mining workflows.
- Filtering Notre système extrait de ce fichier graphe( Workflow de l'utilisateur) un ensemble de mot ( c'est l'ensemble de mots existant das les labels des noeuds d'activité; attention un label peut comporter plus d'un mot concatenés par un séparateur. on extrait la liste de mots completes. puis nous la soumettons a JaW api de wordnet il nous renvois la liste de tous les synonymes pour chaque mot. la nous avons une liste sémantiquement enrichie. on fait une recherche à partir de cette derniere liste, si un workflow contient mot de cette liste il est retenu.
- The concept is simple; Firstly the user enter its workflow (sub-workflow) in an XML format, we transform it into graph format then we extract the list of unique words mentionned in all the labels of the workflow. We estbalish a list of the kaywords and their synonymsthanks to wordnet (Java API for WordNet Searching (JAWS) to retrieve the synsets of a given label from WordNet ). After what we select from the repository only the BP/Workflows that matches at least one from the last list.
- The challenges to be addressed are the following : – Which mining algorithm to employ for finding frequent patterns in the repository? – Which graph representation is best suited for formatting workflows for mining frequent fragments? –how to deal with the heterogeneity of the labels used by different users to model the activities of their workflows within the repository?
- We conducted two experiments. The first aims to validate our proposed representation model D/D1 and to show the drawbacks of the other models. The second experiment aims to validate the filter. We compare the efficiency and effectiveness of the models.On the effectiveness plan, We focus on proving the drawback of the representation model C when it comes to extract recurrent fragments that contain the XOR link .SO, we manually created a synthetic dataset which ensures that the following sub-structure is the most recurrent. As the size of the synthetic dataset is limited ( 9 BP) we extend it to three dataset by adding some workflows from the Taverna 1 repository, while preserving the goal that the most recurrent sub-workflow is the one already presented . we compared the efficiency and effectiveness of the representation models. The second experiment assesses the impact of the semantic filter.
- A is the most expensive in term of space disk required to encode the base in graph format. Concerning the C model as expected: it required more than twice (the number of edges and nodes) the bits required by the model that we propose, namely D and D1, however this ratio decreases to rich between a quarter to the tenth with larger bases. This decrease is due to the content of these bases, with a low percentage of BP with XOR nodes. In third position comes the Model B, it requires less than between 25% up to 40% more than the model D and D1 in terms of number of nodes, edges and bits used. Models D and D1 require the same number of edges and nodes to encode the input data, however the labeling of edges consumes more bits to be encoded.
- .We don't care about correctly classifying negative instances, you just don't want too many of them polluting our results. Model C: concerning these experimentation, as expected the Model C led to the worst qualitative performances. C performs a recall rate that varies between 0% and 61.54% and an average recall around 35%; The model C can, at best, discover only one alternative at time(in our case there is 2 alternatives attached to the XOR node) . Model A:The top extracted substructures are more significant than that of model C, and less significant than other models. However when it comes to larger sized databases, results show a dramatic decline in the quality of its sub-structures reaching 0% in terms of precision and recall; which means there is no extracted substructure related to the user expectation. This limitation, can be explained by the excessive use of control nodes. On large input data, their percentage becomes quite significant leading Subdue algorithm to consider them as important sub-structures. Model B :The model B performs much better than the previous two models, A and C. In fact, The model B retrieved successfully almost 67% of the BP elements of the target sub-structure. more than two time than model C and between 13 to 66% more than model A. Comparing model B to model D; In the other side, models B and D led to very similar accuracy performances. Although, the Model B was able to discover more relevant BP elements than model D (about 10% more), it returned more useless or irrelevant BP elements(around 7%). labeling the edges lead to specializations of the same abstract workflow template and consequently affects the quality of results returned (decrease recall). Model D: We can notice a common performance between models D and D1,which distinguish them from other models. Both of them led to a good precision rate. This performance is due to the fact that these two models do not use control nodes and thereby avoid a negative inference on the results. On large input data their percentage becomes quite significant leading Subdue algorithm to consider as significant typical sub-structures of the coding scheme of the model rules (decrease Precison). The results of the first experiment show clearly that the model D1 records the best performances on all the levels without exception. TP+TN/TP+TN+FP+FN
- .We don't care about correctly classifying negative instances, you just don't want too many of them polluting our results. Model C: concerning these experimentation, as expected the Model C led to the worst qualitative performances. C performs a recall rate that varies between 0% and 61.54% and an average recall around 35%; The model C can, at best, discover only one alternative at time(in our case there is 2 alternatives attached to the XOR node) . Model A:The top extracted substructures are more significant than that of model C, and less significant than other models. However when it comes to larger sized databases, results show a dramatic decline in the quality of its sub-structures reaching 0% in terms of precision and recall; which means there is no extracted substructure related to the user expectation. This limitation, can be explained by the excessive use of control nodes. On large input data, their percentage becomes quite significant leading Subdue algorithm to consider them as important sub-structures. Model B :The model B performs much better than the previous two models, A and C. In fact, The model B retrieved successfully almost 67% of the BP elements of the target sub-structure. more than two time than model C and between 13 to 66% more than model A. Comparing model B to model D; In the other side, models B and D led to very similar accuracy performances. Although, the Model B was able to discover more relevant BP elements than model D (about 10% more), it returned more useless or irrelevant BP elements(around 7%). labeling the edges lead to specializations of the same abstract workflow template and consequently affects the quality of results returned (decrease recall). Model D: We can notice a common performance between models D and D1,which distinguish them from other models. Both of them led to a good precision rate. This performance is due to the fact that these two models do not use control nodes and thereby avoid a negative inference on the results. On large input data their percentage becomes quite significant leading Subdue algorithm to consider as significant typical sub-structures of the coding scheme of the model rules (decrease Precison). The results of the first experiment show clearly that the model D1 records the best performances on all the levels without exception. TP+TN/TP+TN+FP+FN
- The model A is the most expensive in terms of execution time, around 55 up to 25 more time than model D and D1. Let compare the other models. Although on the qualitative level, model B performs better than model C model C seems to be far less expensive. As expected the model D and D1 led to very performances, whereas model D1 performs slightly better.
- The results of the second experimentation shows that the use of the semantic filter caused a reduction of in the input date size (bits) 99% which dramatically improved the execution time 36 times less.
- Decrease the Disk-space Decrease the RAM Decrease the Execution time Increase the quality of results