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  <doc>
    <id>2767</id>
    <completedYear>2021</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>423</pageFirst>
    <pageLast/>
    <pageNumber>40</pageNumber>
    <edition/>
    <issue>10</issue>
    <volume>12</volume>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-03</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Method to Address Complexity in Organizations Based on a Comprehensive Overview</title>
    <parentTitle language="eng">Information 2021</parentTitle>
    <identifier type="doi">10.3390/info12100423</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0</licence>
    <author>Aleksandra Revina</author>
    <author>Ünal Aksu</author>
    <author>Vera G. Meister</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>organizational complexity</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>technological complexity</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>textual complexity</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>morphological box</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>goal question metric</value>
    </subject>
    <collection role="ddc" number="004">Datenverarbeitung; Informatik</collection>
    <collection role="ddc" number="658">Allgemeines Management</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="">Fachbereich Wirtschaft</collection>
    <collection role="Hochschulbibliografie" number="1">Hochschulbibliografie</collection>
    <thesisPublisher>Technische Hochschule Brandenburg</thesisPublisher>
  </doc>
  <doc>
    <id>2817</id>
    <completedYear>2022</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-07-18</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Towards a Business Process Complexity Analysis Framework Based on Textual Data and Event Logs</title>
    <parentTitle language="eng">17th International Conference on Wirtschaftsinformatik, February 2022, Nürnberg, Germany</parentTitle>
    <identifier type="url">https://aisel.aisnet.org/wi2022/bpm/bpm/4</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>Aleksandra Revina</author>
    <author>Ünal Aksu</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Business process analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>complexity</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>event log</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>textual data</value>
    </subject>
    <collection role="ddc" number="004">Datenverarbeitung; Informatik</collection>
    <collection role="ddc" number="658">Allgemeines Management</collection>
    <collection role="institutes" number="">Fachbereich Wirtschaft</collection>
    <collection role="Hochschulbibliografie" number="1">Hochschulbibliografie</collection>
    <thesisPublisher>Technische Hochschule Brandenburg</thesisPublisher>
  </doc>
  <doc>
    <id>3047</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>114</issue>
    <volume/>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2024-02-06</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">An approach for analyzing business process execution complexity based on textual data and event log</title>
    <abstract language="eng">With the advent of digital transformation, organizations increasingly rely on various information systems to support their business processes (BPs). Recorded data, including textual data and event log, expand exponentially, complicating decision-making and posing new challenges for BP complexity analysis in Business Process Management (BPM). Herein, Process Mining (PM) serves to derive insights based on historic BP execution data, called event log. However, in PM, textual data is often neglected or limited to BP descriptions. Therefore, in this study, we propose a novel approach for analyzing BP execution complexity by combining textual data serving as an input at the BP start and event log. The approach is aimed at studying the connection between complexities obtained from these two data types. For textual data-based complexity, the approach employs a set of linguistic features. In our previous work, we have explored the design of linguistic features favorable for BP execution complexity prediction. Accordingly, we adapt and incorporate them into the proposed approach. Using these features, various machine learning techniques are applied to predict textual data-based complexity. Moreover, in this prediction, we show the adequacy of our linguistic features, which outperformed the linguistic features of a widely-used text analysis technique. To calculate event log-based complexity, the event log and relevant complexity metrics are used. Afterward, a correlation analysis of two complexities and an analysis of the significant differences in correlations are performed. The results serve to derive recommendations and insights for BP improvement. We apply the approach in the IT ticket handling process of the IT department of an academic institution. Our findings show that the suggested approach enables a comprehensive identification of BP redesign and improvement opportunities.</abstract>
    <parentTitle language="eng">Information Systems</parentTitle>
    <identifier type="url">https://www.sciencedirect.com/science/article/pii/S0306437923000200?via%3Dihub</identifier>
    <identifier type="doi">10.1016/j.is.2023.102184</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0</licence>
    <author>Aleksandra Revina</author>
    <author>Ünal Aksu</author>
    <collection role="institutes" number="">Fachbereich Technik</collection>
    <collection role="Hochschulbibliografie" number="1">Hochschulbibliografie</collection>
    <thesisPublisher>Technische Hochschule Brandenburg</thesisPublisher>
  </doc>
  <doc>
    <id>3050</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>466</issue>
    <volume/>
    <type>bookpart</type>
    <publisherName>Springer Cham</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2024-02-06</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Towards a Framework for Context Awareness Based on Textual Process Data: Case Study Insights</title>
    <abstract language="eng">Context awareness is critical for the successful execution of processes. In the abundance of business process management (BPM) research, frameworks exclusively devoted to extracting context from textual process data are scarce. With the deluge of textual data and its increasing value for organizations, it becomes essential to employ relevant text analytics techniques to increase the awareness of process workers, which is important for process execution. The present paper addresses this demand by developing a framework for context awareness based on process executions-related textual data using a well-established layered BPM context model. This framework combines and maps various text analytics techniques to the layers of the context model, aiming to increase the context awareness of process workers and facilitate informed decision-making. The framework is applied in an IT ticket processing case study. The findings show that contextual information obtained using our framework enriches the awareness of process workers regarding the process instance urgency, complexity, and upcoming tasks and assists in making decisions in terms of these aspects.</abstract>
    <parentTitle language="eng">Lecture Notes in Business Information Processing</parentTitle>
    <identifier type="doi">https://doi.org/10.1007/978-3-031-26886-1_2</identifier>
    <identifier type="isbn">978-3-031-26885-4 (Print) 978-3-031-26886-1 (online)</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0</licence>
    <author>Aleksandra Revina</author>
    <author>Nina Rizun</author>
    <author>Ünal Aksu</author>
    <collection role="institutes" number="">Fachbereich Technik</collection>
    <collection role="Hochschulbibliografie" number="1">Hochschulbibliografie</collection>
  </doc>
</export-example>
