Evolving Objects in Temporal Information Systems

This paper presents a semantic foundation of temporal conceptual models used to design temporal information systems. We consider a modelling language able to express both timestamping and evolution constraints. We conduct a deeper investigation of evolution constraints, eventually devising a model-theoretic semantics for a full-fledged model with both timestamping and evolution constraints. The proposed formalization is meant both to clarify the meaning of the various temporal constructors that appeared in the literature and to give a rigorous definition, in the context of temporal information systems, to notions like satisfiability, subsumption and logical implication. Furthermore, we show how to express temporal constraints using a subset of first-order temporal logic, i.e. DLRUS, the description logic DLR extended with the temporal operators Since and Until. We show how DLRUS is able to capture the various modelling constraints in a succinct way and to perform automated reasoning on temporal conceptual models

Providing Integrated Life Cycle Support in Process-Aware Information Systems

The need for more flexibility of process-aware information systems (PAISs) has been discussed for several years and different approaches for adaptive process management have emerged. However, only few of them provide support for both changes of individual process instances and the propagation of process type changes to a collection of related process instances. Furthermore, knowledge about process changes has not yet been exploited by any of these systems. This paper presents the ProCycle approach which overcomes this practical limitation by capturing the whole process life cycle and all kinds of changes in an integrated way. Users are not only allowed to deviate from the predefined process in exceptional situations, but are also assisted in retrieving and reusing knowledge about previously performed changes in this context. If similar instance deviations occur frequently, process engineers will be supported in deriving improved process models from them. This, in turn, allows engineers to evolve the PAIS (including the knowledge about the changes) over time. Feasability of the ProCycle approach is demonstrated by a proof-of-concept prototype which combines adaptive process management technology with concepts and methods provided by case-based reasoning (CBR) technology

Realizing Adaptive Process-aware Information Systems with ADEPT2

In dynamic environments it must be possible to quickly implement new business processes, to enable ad-hoc deviations from the defined business processes on-demand (e.g., by dynamically adding, deleting or moving process activities), and to support dynamic process evolution (i.e., to propagate process schema changes to already running process instances). These fundamental requirements must be met without affecting process consistency and robustness of the process-aware information system. In this paper we describe how these challenges have been addressed in the ADEPT2 process management system. Our overall vision is to provide a next generation technology for the support of dynamic processes, which enables full process lifecycle management and which can be applied to a variety of application domains

Data Quality-A Key to Successfully Implementing ECSS

In response to the Expeditionary Logistics for the 21st Century (eLog21) campaign initiatives published in 2003, the United States Air Force (USAF) pursued the acquisition of technology to help transform its logistics processes. With process mapping complete and a proposed roll-out schedule, forward progress towards full implementation of the Expeditionary Combat Support System (ECSS) continues. As a key enabler to achieving eLog21 initiatives, implementing ECSS will help transform current USAF logistics business processes. Integrating more than 450 legacy systems, and with a projected end-state in excess of 750,000 primary, secondary, and tertiary users, ECSS is the largest enterprise resource planning (ERP) system implementation in the world. While the driving force behind an ERP system implementation is exploitation of the numerous benefits associated with transforming business processes, there are several key challenges to address which can mean the difference between success and failure. Data quality is one critical factor in the successful implementation of any ERP system. It is a key to optimizing system performance while maintaining an uninterrupted and acceptable level of support to the war fighter. This research evaluates data quality, focusing on the completeness and consistency of the data, in selected USAF legacy systems. Specifically, this study identifies invalid entries in the source data and also compares item record data between source (D043A) and downstream client (SBSS). This analysis lays the foundation for developing an action plan to allocate resources in an efficient and effective manner to support cleansing the legacy system data prior to migration into ECSS

Quarry: A user-centered big data integration platform

Obtaining valuable insights and actionable knowledge from data requires cross-analysis of domain data typically coming from various sources. Doing so, inevitably imposes burdensome processes of unifying different data formats, discovering integration paths, and all this given specific analytical needs of a data analyst. Along with large volumes of data, the variety of formats, data models, and semantics drastically contribute to the complexity of such processes. Although there have been many attempts to automate various processes along the Big Data pipeline, no unified platforms accessible by users without technical skills (like statisticians or business analysts) have been proposed. In this paper, we present a Big Data integration platform (Quarry) that uses hypergraph-based metadata to facilitate (and largely automate) the integration of domain data coming from a variety of sources, and provides an intuitive interface to assist end users both in: (1) data exploration with the goal of discovering potentially relevant analysis facets, and (2) consolidation and deployment of data flows which integrate the data, and prepare them for further analysis (descriptive or predictive), visualization, and/or publishing. We validate Quarry’s functionalities with the use case of World Health Organization (WHO) epidemiologists and data analysts in their fight against Neglected Tropical Diseases (NTDs).This work is partially supported by GENESIS project, funded by the Spanish Ministerio de Ciencia, Innovación y Universidades under project TIN2016-79269-R.Peer ReviewedPostprint (author's final draft