Organizational Data Mining (ODM)

Nemati, Hamid R.; Barko, Christopher D.

doi:10.4018/978-1-59140-134-6.ch001

Cited by 3 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…& Singh, 2009;Zhang, 2010). There also has been a realization that we need tools that look beyond process automation to a more comprehensive notion of support for organizational processes (Ingvaldsen & Gulla, 2011;Nemati & Barko, 2004;Ramanathan, 2005). This includes giving humans more insight into decision making activities for computer-supported organizational processes and allowing such processes to be flexible and adaptable.…”

Section: Related Workmentioning

confidence: 99%

Stakeholder Ontology and Mining for Improving Complex Services

Ramanathan

Ramnath

2013

International Journal of Information Systems in the Service Sector

View full text Add to dashboard Cite

Complex service-oriented organizations (such as IT customer service or the hospital emergency) deal with many challenges due to incoming request types that we characterize as non-routine. Each such request reflects significant variations in the environment and consequently requirements, which drives discovery of processing needs. At the same time such organizations are often challenged with sharing high-cost resources and satisfying multiple stakeholders with different expectations. Performance improvement in this context is particularly challenging and requires new methods. To address this, the authors present an ontology designed for highly dynamic service organizations where traceable workflow data is difficult to obtain and there are many stakeholders. The ontology provides the contextual framework by with useful knowledge can be successfully extracted from mined performance data obtained from scattered sources. Specifically the service ontology 1) obtains tacit knowledge as explicit in-the-micro feedback from workers performing Roles, 2) provides the structure for organizing in-the-small execution data from evolving process and instances, and 3) aggregates process instances metrics into a performance and decision-making facility to align to in-the-large goals of stakeholders. Using actual customer service requests they illustrate the benefits of the ontology for relating aggregated goals to feedback from individual roles of workers. The authors also illustrate the benefits in terms of identifying actionable improvement targets.

show abstract

Section: Related Workmentioning

confidence: 99%

Stakeholder Ontology and Mining for Improving Complex Services

Ramanathan

Ramnath

2013

International Journal of Information Systems in the Service Sector

View full text Add to dashboard Cite

show abstract

“…Since the early work of Moreno (1934), sociometry, and Social Network Analysis (SNA) in particular, have been active research domains. There is a vast amount of textbooks, research papers, and tools available in this domain (Bavelas, 1948;Bernard et al, 1990;Burt & Minor, 1983;Feldman, 1987;Freeman, 1977Freeman, , 1979Moreno, 1934;Nemati & Barko, 2003;Scott, 1992;Wasserman & Faust, 1994). There have been many studies analyzing organizational activity based on insights from social network analysis.…”

Section: Related Workmentioning

confidence: 99%

“…E-mail is widely used for communication inside organizations and between organizations. Analysis of e-mail communication (Farnham, Portnoy & Turski, 2004;Fisher & Dourish, 2004;Nardi, Whittaker, Isaacs, Creech, Johnson & Hainsworth, 2002) is a popular topic of research in social sciences, in particular sociometry (Bavelas, 1948;Bernard, Killworth, McCarty, Shelley & Robinson, 1990;Burt & Minor, 1983;Feldman, 1987;Freeman, 1977Freeman, , 1979Moreno, 1934;Nemati & Barko, 2003;Scott, 1992;Wasserman & Faust, 1994). If the tasks in the work processes of the organization involve different employees, then they need to communicate to perform the business process.…”

mentioning

confidence: 99%

Mining E-Mail Messages

Aalst

Nikolov

2008

International Journal of Intelligent Information Technologies

View full text Add to dashboard Cite

Increasingly information systems log historic information in a systematic way. Workflow management systems, but also ERP, CRM, SCM, and B2B systems often provide a so-called "event log" (i.e., a log recording the execution of activities). Thus far, process mining has been mainly focusing on structured event logs resulting in powerful analysis techniques and tools for discovering process, control, data, organizational, and social structures from event logs. Unfortunately, many work processes are not supported by systems providing structured logs. Instead, very basic tools such as text editors, spreadsheets, and e-mail are used. This article explores the application of process mining to e-mail (i.e., unstructured or semi-structured e-mail messages are converted into event logs suitable for application of process mining tools). This article presents the tool EMailAnalyzer, embedded in the ProM process mining framework, which analyzes and transforms e-mail messages to a format that allows for analysis using our process mining techniques. The main innovative aspect of this work is that, unlike most other work in this area, our analysis is not restricted to social network analysis. Based on e-mail logs, we can also discover interaction patterns and processes.

show abstract

“…Based on this observation, data-driven learning models have been proposed to represent dynamic relations and uncertain context in organizations. [57,58] In particular, much of the research to date has focused on task-based organizational modeling. [57,59] These strategies aim to infer the network of relations between people, resources, and the tasks that constrain and enable organizational behavior.…”

Section: Introductionmentioning

confidence: 99%

“…[62,63] As such, it is important that the relational behaviors learned be sufficiently stable to reflect the expectations of the organizations. [57] To this effect, various data-driven approaches [58,65–67] have been proposed to mine organizational behaviors that are relatively consistent over time. They capture and analyze complex relationships in the collaborative environment data, and evaluate the consistence of these relations over the time.…”

Section: Introductionmentioning

confidence: 99%

We work with them? Healthcare workers interpretation of organizational relations mined from electronic health records

Chen

Lorenzi

Nyemba

et al. 2014

International Journal of Medical Informatics

View full text Add to dashboard Cite

Objective Models of healthcare organizations (HCOs) are often defined up front by a select few administrative officials and managers. However, given the size and complexity of modern healthcare systems, this practice does not scale easily. The goal of this work is to investigate the extent to which organizational relationships can be automatically learned from utilization patterns of electronic health record (EHR) systems. Method We designed an online survey to solicit the perspectives of employees of a large academic medical center. We surveyed employees from two administrative areas: 1) Coding & Charge Entry and 2) Medical Information Services and two clinical areas: 3) Anesthesiology and 4) Psychiatry. To test our hypotheses we selected two administrative units that have work-related responsibilities with electronic records; however, for the clinical areas we selected two disciplines with very different patient responsibilities and whose accesses and people who accessed were similar. We provided each group of employees with questions regarding the chance of interaction between areas in the medical center in the form of association rules (e.g., Given someone from Coding & Charge Entry accessed a patient’s record, what is the chance that someone from Medical Information Services access the same record?). We compared the respondent predictions with the rules learned from actual EHR utilization using linear-mixed effects regression models. Results The findings from our survey confirm that medical center employees can distinguish between association rules of high and non-high likelihood when their own area is involved. Moreover, they can make such distinctions between for any HCO area in this survey. It was further observed that, with respect to highly likely interactions, respondents from certain areas were significantly better than other respondents at making such distinctions and certain areas’ associations were more distinguishable than others. Conclusions These results illustrate that EHR utilization patterns may be consistent with the expectations of HCO employees. Our findings show that certain areas in the HCO are easier than others for employees to assess, which suggests that automated learning strategies may yield more accurate models of healthcare organizations than those based on the perspectives of a select few individuals.

show abstract

Organizational Data Mining (ODM)

Cited by 3 publications

References 0 publications

Stakeholder Ontology and Mining for Improving Complex Services

Stakeholder Ontology and Mining for Improving Complex Services

Mining E-Mail Messages

We work with them? Healthcare workers interpretation of organizational relations mined from electronic health records

Contact Info

Product

Resources

About