Process Matching Techniques

Beheshti, Seyed-Mehdi-Reza; Benatallah, Boualem; Sakr, Sherif; Grigori, Daniela; Motahari-Nezhad, Hamid Reza; Barukh, Moshe Chai; Gater, Ahmed; Ryu, Seung Hwan

doi:10.1007/978-3-319-25037-3_3

Cited by 4 publications

(3 citation statements)

References 79 publications

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“…(2) Define the requirements; it was defined the r ence models, SCOR and EM, used in this study. Moreover, an analysis was performe the feasibility of integrating both reference models and the requirements for their ad tion based on the Matching Process Technique by Beheshti et al [26] (cf. Section 3.2 Design and develop the artifact; it was designed the MiSCOR following a top-dow pansion technique [27,28] (cf.…”

Section: Research Aim and Methodologymentioning

confidence: 99%

Modelling the Make Process of the Mineral Supply Chain Upstream Segment

Castillo-Villagra,

Icarte,

Thoben

2023

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The upstream segment of the minerals supply chain (MiSC) is the backbone for achieving the transition to clean energy by securing the supply of minerals. Recently, the MiSC has been disrupted, affecting mineral supply and generating uncertainty among clean energy producers. Supply chain (SC) mapping addresses this uncertainty, providing a holistic end-to-end view. However, MiSC mapping studies mainly focus on the downstream segment, leaving aside the upstream segment. In this context, the fundamental gap is the lack of standardized modelling frameworks that can accurately represent the upstream segment dynamics and, at the same time, seamlessly integrate with the downstream segment. This paper bridges this gap, designing a model for the MiSC upstream segment (MiSCOR). Framed within design science research methodology, MiSCOR is built by adapting two reference models, the Exploration and Mining Business Reference Model (EM) and the Supply Chain Operations Reference Model (SCOR), focusing on the commodity-making process. The MiSCOR demonstrates its applicability and robustness in real operational scenarios of one of the largest copper producers. MiSCOR provides a management tool for practitioners and decision-makers in the MISC’s upstream segment, offering blueprints to throw functional silos, foster collaboration, and a standardized framework that integrates seamlessly with the downstream segment.

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Section: Research Aim and Methodologymentioning

confidence: 99%

Modelling the Make Process of the Mineral Supply Chain Upstream Segment

Castillo-Villagra,

Icarte,

Thoben

2023

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“…This gives rise to integrated models for processes and data which in turn opens up the possibility for their analysis and verification [3,4]. Notably, these integrated models can also be distributed in Big Data environments [5].…”

Section: Introductionmentioning

confidence: 99%

Verifying the manipulation of data objects according to business process and data models

et al. 2020

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Business processes read and write data objects, usually stored in databases. Although data models and activity-oriented business process models originate from different paradigms, they need to work together properly. The data object states are transformed during each process instance by the activities of the process model. It is therefore necessary to verify whether the states of the data objects are correct according to the process model, and to discover the states of the stored data objects. This implies determining the relation between the data objects stored in the database, the data objects involved in the process, and the activities that within the business process that create the data objects and modify their states. In order to verify the business process annotated with data states and to reduce the existing gap between data model and business process model, we propose a methodology that includes enlarging the capability to describe data states in business processes; verifying the completeness and consistency of the data states described in accordance with their relation to the business process model; and discovering the states of the data objects stored in the database according to the business process model where they are managed. The methodology is supported by a framework that enables a natural-like language to be employed to describe the states, to apply the necessary algorithms to verify the consistency and completeness of the model, and to determine the states of the stored data objects according to the model described. To validate our proposal, an extension of Activiti TM has been implemented and applied to a real example as an illustration of its applicability.Keywords Business processes • Integration of data and processes • Data object state • Object-relational mapping • Data state verification Business processes (BPs) and their continuous improvement are fundamental to the operation of companies. For a wide range of enterprises, business process analytics, validation, and verification are a key endeavour [1]. The automation of processes offers an opportunity to B José Miguel Pérez-Álvarez

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“…In this context, the data curation will enable data scientists to understand and analyze the big data, understand it better and extract more value and insights. In particular, applying analytics -such as process analytics [23,16,52,21,24,22,14,64], information networks analysis [18,13,42,12,28,19,60,1], data and metadata analysis [58,17,27,26,20] -to the curated data can reveal patterns, trends, and associations and consequently increase the added value and insights of the raw data. Trending applications include but not limited to improve government services [36,35]; predict intelligence activities [69,38]; unravel human trafficking activities [32,4,6]; understand impact of news on stock markets [31,5]; analysis of financial risks [33,7]; accelerate scientific discovery [68,44]; as well as to improve national security and public health [65,47].…”

Section: Introductionmentioning

confidence: 99%

Data Curation APIs

Beheshti¹,

Tabebordbar²,

Benatallah³

et al. 2016

Preprint

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Understanding and analyzing big data is firmly recognized as a powerful and strategic priority. For deeper interpretation of and better intelligence with big data, it is important to transform raw data (unstructured, semi-structured and structured data sources, e.g., text, video, image data sets) into curated data: contextualized data and knowledge that is maintained and made available for use by end-users and applications. In particular, data curation acts as the glue between raw data and analytics, providing an abstraction layer that relieves users from time consuming, tedious and error prone curation tasks. In this context, the data curation process becomes a vital analytics asset for increasing added value and insights.In this paper, we identify and implement a set of curation APIs and make them available 1 (on GitHub 2 ) to researchers and developers to assist them transforming their raw data into curated data. The curation APIs enable developers to easily add features -such as extracting keyword, part of speech, and named entities such as Persons, Locations, Organizations, Companies, Products, Diseases, Drugs, etc.; providing synonyms and stems for extracted information items leveraging lexical knowledge bases for the English language such as WordNet; linking extracted entities to external knowledge bases such as Google Knowledge Graph and Wikidata; discovering similarity among the extracted information items, such as calculating similarity between string, number, date and time data; classifying, sorting and categorizing data into various types, forms or any other distinct class; and indexing structured and unstructured data -into their applications.1 We encourage researchers/developers to cite our work if you have used our APIs, libraries, tools or datasets.

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Process Matching Techniques

Cited by 4 publications

References 79 publications

Modelling the Make Process of the Mineral Supply Chain Upstream Segment

Modelling the Make Process of the Mineral Supply Chain Upstream Segment

Verifying the manipulation of data objects according to business process and data models

Data Curation APIs

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