RLRecommender: A Representation-Learning-Based Recommendation Method for Business Process Modeling

Wang, Huaqing; Wen, Lijie; Li, Lin; Wang, Jianmin

doi:10.1007/978-3-030-03596-9_34

Cited by 6 publications

(6 citation statements)

References 3 publications

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“…Deng et al [13] developed a recommendation system to generate a sorted candidate node sets, which used a subgraph mining method to extract patterns from process repositories. Wang et al [16] utilized the properties of business process repositories and proposed a representationlearning-based recommendation method.…”

Section: Related Workmentioning

confidence: 99%

“…In Algorithm 3, we first initiate the matrix dist according to the matrix Matrix (line 1) and initiate the value of cutoff distance dc according to the rule of thumb introduced in [10] (line 2). en, we calculate the local density values of scientific workflows (lines 3-10) and their relative distances values (lines [11][12][13][14][15][16][17][18]. Finally, we can apply the DPC algorithm to divide scientific workflows into different clusters (line 19), where each cluster in the Clusters can be denoted as a group of scientific workflows with a scientific workflow as its cluster center.…”

Section: Dpc-based Clustering Of Scientific Workflowsmentioning

confidence: 99%

“…Example: (1) dist ⟵ 1 − Matrix (2) dc ⟵ select a value from the dist so that the number of values below it is around 1 to 2% of the total number of values in the dist (3) for each scientific workflow sw i in SW do (4) ld i ⟵ 0 (5) for each scientific workflow sw j in SW do (6) if dist i,j < dc then (7) ld i ⟵ ld i + 1 (8) end if (9) end for (10) end for (11) for each scientific workflow sw i do (12) rd i ⟵ max(dist) (13) for each scientific workflow sw j do (14) if ld i < ld j and rd i < dist i,j then (15) rd i ⟵ dist i,j (16) end if (17) end for (18) 8 Complexity respectively, where the working procedure of the function cosine_sim in lines 6 and 13 is similar to that of equation (6). Besides, for each description in requirement.dscs, we search the best matching activity (lines 5-11) and the best matching subscientific workflow for it (lines 12-18), then we choose the better one for constructing a sample science workflow (lines 20-24).…”

Section: Retrieval Of Appropriate Activities and Subscientificmentioning

confidence: 99%

“…Figure 1, there exists a subscientific workflow named CNTCI, which is short for Chemical_Name_To_Chemspider_ID, and a subscientific workflow named Workflow40 in the scientific workflow sw 1 . We can get the textual descriptions of the sw 1 , i.e., " is workflow will map a chemical name or identifier to uniform sim tmp1 ⟵ 0, sim tmp2 ⟵ 0 (4) for each sw in SW do (5) for each activity a in sw do (6) sim ⟵ cosine_sim (doc2vec (dsc), doc2vec (a)) (7) if sim > sim tmp1 then (8) sim tmp1 ⟵ sim (9) a tmp ⟵ a (10) end if (11) end for (12) for each sub-scientific workflow d in sw do (13) sim ⟵ cosine_sim (doc2vec (dsc), doc2vec (d)) (14) if sim > sim tmp2 then (15) sim tmp2 ⟵ sim (16) d tmp ⟵ d (17) end if (18) end for (19) end for (20) if sim tmp1 > sim tmp2 then (21) append a tmp to A smp (22) else (23) append d tmp to D smp (24) end if (25) end for (26) return A smp , D smp ALGORITHM 4: Function GetActivity_SubWF (requirement.dscs, SW).…”

Section: An Example On Textualmentioning

confidence: 99%

“…To evaluate the efficiency of scientific workflow recommendations, we adopt the precision and recall measures used in [18] and the F 1 score used in [16] as our evaluation metrics, which are described as equations (8)-(10), respectively: Input: (i) sw smp : a sample scientific workflow. (ii) Clusters: the set of scientific workflow clusters.…”

Section: Evaluation Metricsmentioning

confidence: 99%

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Heterogeneous Information Network-Based Scientific Workflow Recommendation for Complex Applications

et al. 2020

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Scientific workflow is a valuable tool for various complicated large-scale data processing applications. In recent years, the increasingly growing number of scientific processes available necessitates the development of recommendation techniques to provide automatic support for modelling scientific workflows. In this paper, with the help of heterogeneous information network (HIN) and tags of scientific workflows, we organize scientific workflows as a HIN and propose a novel scientific workflow similarity computation method based on metapath. In addition, the density peak clustering (DPC) algorithm is introduced into the recommendation process and a scientific workflow recommendation approach named HDSWR is proposed. The effectiveness and efficiency of our approach are evaluated by extensive experiments with real-world scientific workflows.

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Section: Related Workmentioning

confidence: 99%