Mining Goal Refinement Patterns: Distilling Know-How from Data
06 Nov 2017-pp 69-76
TL;DR: A means for mining patterns from enterprise event logs and a technique to leverage vector representations of words and phrases to compose these patterns to obtain complete goal models are offered.
Abstract: Goal models play an important role by providing a hierarchic representation of stakeholder intent, and by providing a representation of lower-level subgoals that must be achieved to enable the achievement of higher-level goals. A goal model can be viewed as a composition of a number of goal refinement patterns that relate parent goals to subgoals. In this paper, we offer a means for mining these patterns from enterprise event logs and a technique to leverage vector representations of words and phrases to compose these patterns to obtain complete goal models. The resulting machinery can be quiote powerful in its ability to mine know-how or constitutive norms. We offer an empirical evaluation using both real-life and synthetic datasets.
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TL;DR: A systemic literature review based on 24 papers rigorously selected from four popular search engines in 2018 is provided to assess the state of goal-oriented process mining, highlighting that the use of process mining in association with goals does not yet have a coherent line of research, whereas intention mining shows a meaningful trace of research.
Abstract: Process mining helps infer valuable insights about business processes using event logs, whereas goal modeling focuses on the representation and analysis of competing goals of stakeholders and systems. Although there are clear benefits in mining the goals of existing processes, goal-oriented approaches that consider logs during model construction are still rare. Process mining techniques, when generalizing large instance-level data into process models, can be considered as a data-driven complement to use case/scenario elicitation. Requirements engineers can exploit process mining techniques to find new system or process requirements in order to align current practices and desired ones. This paper provides a systemic literature review, based on 24 papers rigorously selected from four popular search engines in 2018, to assess the state of goal-oriented process mining. Through two research questions, the review highlights that the use of process mining in association with goals does not yet have a coherent line of research, whereas intention mining (where goal models are mined) shows a meaningful trace of research. Research about performance indicators measuring goals associated with process mining is also sparse. Although the number of publications in process mining and goal modeling is trending up, goal mining and goal-oriented process mining remain modest research areas. Yet, synergetic effects achievable by combining goals and process mining can potentially augment the precision, rationality and interpretability of mined models and eventually improve opportunities to satisfy system stakeholders.
40 citations
Cites background or methods from "Mining Goal Refinement Patterns: Di..."
...[92] P8 – 2017 Conf: ER 0 AND/OR graph Yes Yes...
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...[92] aimed to address the question: can enterprise goal models be mined from readily available enterprise data? Note that the method they proposed does not consider mining goals directly from event logs....
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...[92] propose an approach to discover goal refinement patterns of the goal models considering the sequence of events in multi-layered event logs....
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01 Sep 2019
TL;DR: This paper proposes a new Goal-oriented Process Enhancement and Discovery (GoPED) method to align discovered models with stakeholders’ goals and proposes three types of criteria that suggest desired satisfaction levels from a case perspective, goal perspective, and organization perspective.
Abstract: Process mining practices are mainly activity-oriented and they seldom consider the (often conflicting) goals of stakeholders. Involving goal-related factors, as often done in requirements engineering, can improve the rationality and interpretability of mined models and lead to better opportunities to satisfy stakeholders. This paper proposes a new Goal-oriented Process Enhancement and Discovery (GoPED) method to align discovered models with stakeholders’ goals. GoPED first adds goal-related attributes to traditional event characteristics (case identifier, activities, and timestamps), selects a subset of cases with respect to a goal-related criterion, and finally discovers a process model from that subset. We define three types of criteria that suggest desired satisfaction levels from a (i) case perspective, (ii) goal perspective, and (iii) organization perspective. For each criterion, an algorithm is proposed to enable selecting the best subset of cases were the criterion holds. The resulting process models are expected to reproduce the desired level of satisfaction. A synthetic event log is used to illustrate the proposed algorithms and to discuss their results.
4 citations
References
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TL;DR: This paper proposed two novel model architectures for computing continuous vector representations of words from very large data sets, and the quality of these representations is measured in a word similarity task and the results are compared to the previously best performing techniques based on different types of neural networks.
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Proceedings Article•
16 Jan 2013TL;DR: Two novel model architectures for computing continuous vector representations of words from very large data sets are proposed and it is shown that these vectors provide state-of-the-art performance on the authors' test set for measuring syntactic and semantic word similarities.
Abstract: We propose two novel model architectures for computing continuous vector
representations of words from very large data sets. The quality of these
representations is measured in a word similarity task, and the results are
compared to the previously best performing techniques based on different types
of neural networks. We observe large improvements in accuracy at much lower
computational cost, i.e. it takes less than a day to learn high quality word
vectors from a 1.6 billion words data set. Furthermore, we show that these
vectors provide state-of-the-art performance on our test set for measuring
syntactic and semantic word similarities.
9,270 citations
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