Business Process Paradigms

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_2

Cited by 5 publications

(2 citation statements)

References 62 publications

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“…Owing to the immaturity of ACM as an emerging trend, many challenges remain, including those of data integration, a theoretical foundation, authorization and role management, knowledge‐worker empowerment, and knowledge storage and extraction (Beheshti et al, 2016). The implementation and maintenance efforts of a business process using case handling technology might be challenging compared to traditional workflow technologies under certain circumstances (Weber et al, 2010).…”

Section: Acm Applications and Current Challengesmentioning

confidence: 99%

Adaptive case management: An overview

Shahrah

Al‐Mashari

2021

Knowl Process Manag

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Knowledge work is growing in today's marketplace, supported by "knowledge workers," a valuable asset in organizations. Traditional software applications are too rigid to support knowledge work or empower knowledge workers to perform their nonroutine tasks. Therefore, most knowledge work today is managed manually or using ad-hoc technologies such as e-mail and social media. Adaptive case management (ACM) is a new promising approach for effectively supporting knowledge work, empowering knowledge workers, and overcoming the limitations of traditional software applications. Nevertheless, ACM is an emerging field accompanied by confusion and debate among researchers and practitioners. This study aims at providing an overview of the key concepts and principles of ACM. Furthermore, this study outlines the major differences between traditional business process management and ACM.

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Section: Acm Applications and Current Challengesmentioning

confidence: 99%

Adaptive case management: An overview

Shahrah

Al‐Mashari

2021

Knowl Process Manag

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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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