Sense-able process innovation in digital health infrastructures

In this paper, we examine the role of IT in enabling and supporting process innovation at a general hospital in Norway. The motivation for our study is that fragmented and heterogeneous components of digital infrastructure in complex organisational settings hamper the ability to monitor and improve organisational performance through process innovation. Prior research indicates that loose couplings between traditional ‘heavyweight IT’ (resilient, secure, and stable) and ‘lightweight IT’ (consumer-oriented, context-aware and flexible) can support innovation. These principles have not been applied to process innovation. Our research question is, how can lightweight IT extend digital infrastructure to support process innovation, in hospital coordinative practices? We use the sense and respond framework from Overby et al. (2006) to analyze our case findings and derive a model for sense-able process innovation with lightweight IT. The model outlines how lightweight IT extends digital (health) infrastructure and affords an organisational ability to continuously sense and respond to the effects of process innovatio

The non-technical challenges of Location Based Services markets: are the users' concerns being ignored?

Location Based Services (LBS) market is growing rapidly, however it has faced several challenges and issues, including the availability of reliable positioning services seamlessly (indoors and outdoors), the privacy protection issues, and the relatively high demands for resources, such as high power consumption and cost. Among all the issues introduced to the markets of LBS, the non-technical issues can be easier to understand for many of ordinary users of LBS and they, consequently, can become yet bigger challenge to the development of LBS markets. Lack of social acceptance of the LBS applications can result in slowing down the growth of the market, if not failure. This paper reviews the non-technical issues of LBS market from users’ perspective and evaluate the significance of their impact on the growth of the market based on the results of a survey conducted and the predictive analysis have been done

An Introduction to Digital Transformation

Digital transformation has been one of the most studied phenomena in information systems (IS) and organizational science literature. With novel digital technologies emerging at a growing pace, it is important to understand what we have learned in over three decades of research and what we still need to understand in order to harness the full potential of such digital tools. In this chapter, we present a brief overview of digital transformation and develop a conceptual framework which we use as a basis of discussing the extant literature. The conceptual framework is also used as a means of positioning the empirical chapters presented in the rest of this edited volume. Finally, we discuss the role of context in digital transformation and identify some differences that span industry, domain, size class, and country of operation.publishedVersio

Enabling Process Mining in Aircraft Manufactures: Extracting Event Logs and Discovering Processes from Complex Data

Process mining is employed by organizations to completely understand and improve their processes and to detect possible deviations from expected behavior. Process discovery uses event logs as input data, which describe the times of the actions that occur the traces. Currently, Internet-of-Things environments generate massive distributed and not always structured data, which brings about new complex scenarios since data must first be transformed in order to be handled by process min ing tools. This paper shows the success case of application of a solution that permits the transformation of complex semi-structured data of an assembly-aircraft process in order to create event logs that can be man aged by the process mining paradigm. A Domain-Specific Language and a prototype have been implemented to facilitate the extraction of data into the unified traces of an event log. The implementation performed has been applied within a project in the aeronautic industry, and promis ing results have been obtained of the log extraction for the discovery of processes and the resulting improvement of the assembly-aircraft process.Ministerio de Ciencia y Tecnología RTI2018-094283-B-C3

Real-Time Business Process Recommendations

Process Mining is a discipline focused on the analysis of the data logged by the execution of deployed business processes. Business process’ execution is not linear and might entail many decisions that affect the process execution. Decision Mining is a sub-field of Process mining, focused on finding and supporting these decision points. The decision criteria used in these decision points is often not explicit or optimized. Most research, techniques and algorithms in this area have been focused on providing off-line management support as means of explicitly representing implicit decisions. The solution proposed in this document presents a system that will provide the business actors a Best Next Action recommendation during the execution of business processes. To do so, it will be automatically identifying possible decision points, mine its data objects, apply probabilistic supervised learning algorithms and predict the best actions

Towards the Detection of Promising Processes by Analysing the Relational Data

Business process discovery provides mechanisms to extract the general process behaviour from event observations. However, not always the logs are available and must be extracted from repositories, such as relational databases. Derived from the references that exist between the relational tables, several are the possible combinations of traces of events that can be extracted from a relational database. Dif ferent traces can be extracted depending on which attribute represents the case−id, what are the attributes that represent the execution of an activity, or how to obtain the timestamp to define the order of the events. This paper proposes a method to analyse a wide range of possible traces that could be extracted from a relational database, based on measuring the level of interest of extracting a trace log, later used for a discov ery process. The analysis is done by means of a set of proposed metrics before the traces are generated and the process is discovered. This anal ysis helps to reduce the computational cost of process discovery. For a possible case−id every possible traces are analysed and measured. To validate our proposal, we have used a real relational database, where the detection of processes (most and least promising) are compared to rely on our proposal.Ministerio de Ciencia y Tecnología RTI2018-094283-B-C3

An investigation of discovering business processes from operational databases

Process discovery techniques aim to discover process models from event-logs. An event-log records process activities carried out on related data items and the timestamp where the event occurred. While the event-log is explicitly recorded in the process-awareness information systems such as modern ERP and CRM systems, other in-house information systems may not record event-log, but an operational database. This raises the need to develop process discovery solutions from operational databases. Meanwhile, process models can be represented from various perspectives, e.g. functional, behavioural, organisational, informational and business context perspectives. However, none of the existing techniques supports to discover process models from different perspectives using operational databases. This paper aims to deal with these gaps by proposing process expressive artefacts based on process perspectives adopted in the literature, as well as discussing how these artefacts can be extracted from data components of a typical operational database

mobile systems applied to traffic management and safety a state of the art

Abstract Mobile systems applied to traffic management and control and traffic safety have the potential to shape the future of road transportation. The following innovations, that will be deployed on a large scale, could reshape road traffic management practices: – the implementation of connected vehicles with global navigation satellite (GNSS) system receivers; – the autonomous car revolution; – the spreading of smartphone-based systems and the development of Mobile Cooperative Web 2.0 which is laying the base for future development of systems that will also incorporate connected and autonomous vehicles; – an increasing need for sustainability of transportation in terms of energy efficiency, traffic safety and environmental issues. This paper intends to provide a state of the art on current systems and an anticipation of how mobile systems applied to traffic management and safety could lead to a completely new transportation system in which safety and congestion issues are finally properly addressed