25,359 research outputs found
Active Anomaly Detection for Key Item Selection in Process Auditing
No full textProcess mining allows auditors to retrieve crucial information about transactions by analysing the process data of a client. We propose an approach that supports the identification of unusual or unexpected transactions, also referred to as exceptions. These exceptions can be selected by auditors as “key items”, meaning the auditors wants to look further into the underlying documentation of the transaction. The approach encodes the traces, assigns an anomaly score to each trace, and uses the domain knowledge of auditors to update the assigned anomaly scores through active anomaly detection. The approach is evaluated with three groups of auditors over three cycles. The results of the evaluation indicate that the approach has the potential to support the decision-making process of auditors. Although auditors still need to make a manual selection of key items, they are able to better substantiate this selection. As such, our research can be seen as a step forward with respect to the usage of anomaly detection and data analysis in process auditing
Process querying: Enabling business intelligence through query-based process analytics
No full text<b>Highlights</b>\ud
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- A framework for designing process querying methods is proposed\ud
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- The framework is positioned for broader Process Analytics and Business Intelligence\ud
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- The framework is grounded in use cases from the Business Process Management field\ud
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- The framework is informed by and validated via a systematic literature review\ud
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- The framework structures the state of the art and points to gaps in existing research\ud
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<b>Abstract</b>\ud
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The volume of process-related data is growing rapidly: more and more business operations are being supported and monitored by information systems. Industry 4.0 and the corresponding industrial Internet of Things are about to generate new waves of process-related data, next to the abundance of event data already present in enterprise systems. However, organizations often fail to convert such data into strategic and tactical intelligence. This is due to the lack of dedicated technologies that are tailored to effectively manage the information on processes encoded in process models and process execution records. Process-related information is a core organizational asset which requires dedicated analytics to unlock its full potential. This paper proposes a framework for devising process querying methods, i.e., techniques for the (automated) management of repositories of designed and executed processes, as well as models that describe relationships between processes. The framework is composed of generic components that can be configured to create a range of process querying methods. The motivation for the framework stems from use cases in the field of Business Process Management. The design of the framework is informed by and validated via a systematic literature review. The framework structures the state of the art and points to gaps in existing research. Process querying methods need to address these gaps to better support strategic decision-making and provide the next generation of Business Intelligence platforms
Text Analytics for Android Project
No full textMost advanced text analytics and text mining tasks include text classification, text clustering, building ontology, concept/entity extraction, summarization, deriving patterns within the structured data, production of granular taxonomies, sentiment and emotion analysis, document summarization, entity relation modelling, interpretation of the output. Already existing text analytics and text mining cannot develop text material alternatives (perform a multivariant design), perform multiple criteria analysis,
automatically select the most effective variant according to different aspects (citation index of papers (Scopus, ScienceDirect, Google Scholar) and authors (Scopus, ScienceDirect, Google Scholar), Top 25 papers, impact factor of journals, supporting phrases, document name and contents, density of keywords), calculate utility degree and market value. However, the Text Analytics for Android Project can perform the aforementioned functions. To the best of the knowledge herein, these functions have not been previously implemented; thus this is the first attempt to do so. The Text Analytics for Android Project is briefly described in this article
Natural Language Processing: Writing Analytics
No full textWriting analytics uses computational techniques to analyse written texts for the purposes of
improving learning. This chapter provides an introduction to writing analytics, through the
discussion of linguistic and domain orientations to the analysis of writing, and descriptive and
evaluative intentions for the analytics. The chapter highlights the importance of the relationship
between writing analytics and good pedagogy, contending that for writing analytics to positively
impact learning, actionability must be considered in the design process. Limitations of writing
analytics are also discussed, highlighting areas of concern for future researc
Time for Change: Why Learning Analytics Needs Temporal Analysis
No full textLearning is a process that occurs over time: We build understanding, change perspectives, and develop skills over the course of extended experiences. As a field, learning analytics aims to generate understanding of, and support for, such processes of learning. Indeed, a core characteristic of learning analytics is the generation of high-resolution temporal data about various types of actions. Thus, we might expect study of the temporal nature of learning to be central in learning analytics research and applications. However, temporality has typically been underexplored in both basic and applied learning research. As Reimann (2009) notes, although “researchers have privileged access to process data, the theoretical constructs and methods employed in research practice frequently neglect to make full use of information relating to time and order” (p. 239). Typical approaches to analysis often aggregate across data due to a collection of conceptual, methodological, and operational challenges. As described below, insightful temporal analysis requires (1) conceptualising the temporal nature of learning constructs, (2) translating these theoretical propositions into specific methodological approaches for the capture and analysis of temporal data, and (3) practical methods for capturing temporal data features and using analyses to impact learning contexts. There is a pressing need to address these challenges if we are to realize the exciting possibilities for temporal learning analytics.</jats:p
From Symbolic RPA to Intelligent RPA: Challenges for Developing and Operating Intelligent Software Robots
No full textRobotic process automation (RPA) is a novel technology that automates tasks by interacting with other software through their respective user interfaces. The technology has received substantial business attention because of its potential for rapid automation of process-driven tasks that would otherwise require tedious manual labor. This article explores the dichotomy between the practical reality of symbolic RPA, which requires handcrafting robots using process models and rulesets, and the promise of intelligent RPA, which relies on artificial intelligence technology to implement intelligent robots. Our research is based on a scholarly literature review as well as an interview study to derive and discuss challenges for this transition. We found that issues such as the lack of training data, human bias in data, compliance issues with transfer learning, poor explainability of robot decisions, and job-security-induced fear of AI robots all need to be addressed to enable the transition from symbolic to intelligent RPA
Business process improvement activities: differences in organizational size, culture, and resources
No full textAlthough there are many business process improvement (BPI) methods, organizations are struggling to apply them effectively. We answer to the call to focus more on the organizational context in BPI projects. We use workarounds – deviations from the prescribed way of using an information system – as a specific angle to approach BPI. In five healthcare organizations of different contextual types, we study workarounds and make recommendations for process improvements. Based on this explorative multiple-case study, we propose a set of contextual activities for each stage of a BPI project. Thereby, we shed light on the differences in tackling process improvements in organizations that differ in size, culture, and the availability of resources for BPI projects. We evaluate the completeness and expected adoption of the proposed contextual BPI activities by organizing two focus groups and conducting a survey
Process Efficiency - Adapting Flow to the Agile Improvement Effort
No full textIn Scrum, we measure performance using velocity. However, the velocity of one team cannot be compared to the velocity of another, since it is a relative measure that is only of meaning to the team using it. So can we objectively measure the performance of teams?
Measuring Value Added Time as a percentage of Total Time is a metric that is used in Lean Manufacturing to help get a better understanding of production processes and optimize those processes.
This paper introduces an adaptation of this metric to the Agile environment. Giving teams an objective insight into their efficiency helps them optimize their efficiency and compare themselves to other teams. This adapted metric is called Process Efficiency and is comparable across teams, technologies, and domains of practice
Towards a Conceptual Framework for Decomposing Non-functional Requirements of Business Process into Quality of Service Attributes
No full textNon-functional Requirements (NFRs) of web services are defined by IT teams at the implementation level often as Quality of Service (QoS) attributes. Orchestrating web services to run business processes requires a rigorous definition of the NFRs of such web services. The definition of QoS attributes should consider the business process NFRs since misinterpretations of web service NFRs may affect the behavior of the web services and hence achieving the business goals. The approaches proposed so far are still heavily dependent on an IT expert’s knowledge to identify the appropriate QoS attributes required to meet particular business process NFRs. Defining appropriate QoS attributes without reference to business process-level NFRs may be a costly, time-consuming task. We propose a conceptual framework for the hierarchical decomposition of NFRs from the business process level to the web service level. This framework seeks to reduce the dependence on a particular IT expert’s knowledge by simplifying the dialog between the business and IT areas. The proposed framework relies on a structure of NFRs interdependence. The main reference was the ISO/IEC 25010 Product Quality Model, extended by additional software quality models and particular QoS attributes
Robotic Process Automation: Contemporary themes and challenges
No full textThrough the application of Robotic Process Automation (RPA) organisations aim to increase their operational efficiency. In RPA, robots, or ‘bots’ for short, represent software agents capable of interacting with software systems by mimicking user actions, thus alleviating the workload of the human workforce. RPA has already seen significant uptake in practice; solution technologies are offered by multiple vendors. Contrasting with this early practical adoption is the hitherto relative lack of attention to RPA in the academic literature. As a consequence, RPA lacks the sound theoretical foundations that allow for objective reasoning around its application and development. This, in turn, hinders initiatives for achieving meaningful advances in the field. This paper presents a structured literature review that identifies a number of contemporary, RPA-related themes and challenges for future research
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