Semantic data mining and linked data for a recommender system in the AEC industry

Even though it can provide design teams with valuable performance insights and enhance decision-making, monitored building data is rarely reused in an effective feedback loop from operation to design. Data mining allows users to obtain such insights from the large datasets generated throughout the building life cycle. Furthermore, semantic web technologies allow to formally represent the built environment and retrieve knowledge in response to domain-specific requirements. Both approaches have independently established themselves as powerful aids in decision-making. Combining them can enrich data mining processes with domain knowledge and facilitate knowledge discovery, representation and reuse. In this article, we look into the available data mining techniques and investigate to what extent they can be fused with semantic web technologies to provide recommendations to the end user in performance-oriented design. We demonstrate an initial implementation of a linked data-based system for generation of recommendations

Distributed data mining in grid computing environments

The official published version of this article can be found at the link below.The computing-intensive data mining for inherently Internet-wide distributed data, referred to as Distributed Data Mining (DDM), calls for the support of a powerful Grid with an effective scheduling framework. DDM often shares the computing paradigm of local processing and global synthesizing. It involves every phase of Data Mining (DM) processes, which makes the workflow of DDM very complex and can be modelled only by a Directed Acyclic Graph (DAG) with multiple data entries. Motivated by the need for a practical solution of the Grid scheduling problem for the DDM workflow, this paper proposes a novel two-phase scheduling framework, including External Scheduling and Internal Scheduling, on a two-level Grid architecture (InterGrid, IntraGrid). Currently a DM IntraGrid, named DMGCE (Data Mining Grid Computing Environment), has been developed with a dynamic scheduling framework for competitive DAGs in a heterogeneous computing environment. This system is implemented in an established Multi-Agent System (MAS) environment, in which the reuse of existing DM algorithms is achieved by encapsulating them into agents. Practical classification problems from oil well logging analysis are used to measure the system performance. The detailed experiment procedure and result analysis are also discussed in this paper

Reuse, Reduce, Support: Design Principles for Green Data Mining

This paper reports on a design science research (DSR) study that develops design principles for “green” – more environmentally sustainable – data mining processes. Grounded in the Cross Industry Standard Process for Data Mining (CRISP-DM) and on a review of relevant literature on data mining methods, Green IT, and Green IS, the study identifies eight design principles that fall into the three categories of reuse, reduce, and support. The paper develops an evaluation strategy and provides empirical evidence for the principles’ utility. It suggests that the results can inform the development of a more general approach towards Green Data Science and provide a suitable lens to study sustainable computing

Towards an Intelligent Workflow Designer based on the Reuse of Workflow Patterns

In order to perform process-aware information systems we need sophisticated methods and concepts for designing and modeling processes. Recently, research on workflow patterns has emerged in order to increase the reuse of recurring workflow structures. However, current workflow modeling tools do not provide functionalities that enable users to define, query, and reuse workflow patterns properly. In this paper we gather a suite for both process modeling and normalization based on workflow patterns reuse. This suite must be used in the extension of some workflow design tool. The suite comprises components for the design of processes from both legacy systems and process modeling

A Literature Review on Business Process Management

Business Process (BP) is a set of coordinated tasks that define how to achieve organizational goals. It emerges as an efficient tool, whose main goal is supporting the design, administration, setup, disclosure and analysis of business processes, and organizations use it to identify opportunities to reduce costs, increase service or product quality, etc. The goal of BPM is to manage business processes. Organizations wish to manage perfectly these processes instead of fixing the non-ideal process setups or outcomes in a reactive manner. At present, variability management in the business processes domain is considered as a key of reuse. Process mining offers a set of techniques that retrieves information from event logs and gives companies a better understanding of their processes. Process mining has gained significant attention in both research and industry as a range of data mining tools has emerged. In this study, we will provide a systematic literature review from 2017 to 2021; we will use Kitchenham method to conduct this SLR. Data source as IEEE, ACM, Springer and ScienceDirect are used to obtain literature. We had, as a result, 51 papers from 3079 papers to complete this paper. This SLR had for objective to see the research trend on the topics of business process management, improvement, modeling and approaches using data mining

Workflow Patterns for Business Process Modeling

For its reuse advantages, workflow patterns (e.g., control flow patterns, data patterns, resource patterns) are increasingly attracting the interest of both researchers and vendors. Frequently, business process or workflow models can be assembeled out of a set of recurrent process fragments (or recurrent business functions), each of them having generic semantics that can be described as a pattern. To our best knowledge, so far, there has been no (empirical) work evidencing the existence of such recurrent patterns in real workflow applications. Thus, in this paper we elaborate the frequency with which certain patterns occur in practice. Furthermore, we investigate completeness of workflow patterns (based on recurrent functions) with respect to their ability to capture a large variety of business processes

The Sustainable Management of Metals: An Analysis of Global Research

The objective of this study was to analyze research trends in the field of sustainable management of metals on a global level between 1993 and 2017. To do so, a bibliometric analysis was carried out on a total of 6967 articles. The results revealed the growing interest in this research field, particularly over the last five year-period during which 63% of all articles were published. The three journals in which most articles had been published were the Journal of Cleaner Production, ACS Sustainable Chemistry and Engineering, and Chemsuschem. The countries that published the most articles were China, the United States, India, Germany, and the United Kingdom. A sizeable network of collaboration has been established between countries for the joint publication of studies. The main lines of research have been focused on metal decontamination in water and soil, waste management oriented towards reuse and recycling, and the innovation of processes for cleaner and more efficient production. The results revealed the need for comprehensive studies that integrate different disciplines within the same analytical framework, and to promote research that contributes to the different dimensions of sustainability (environmental, economic, and social)

Two-phased knowledge formalisation for hydrometallurgical gold ore process recommendation and validation

This paper describes an approach to externalising and formalising expert knowledge involved in the design and evaluation of hydrometallurgical process chains for gold ore treatment. The objective was to create a case-based reasoning application for recommending and validating a treatment process of gold ores. We describe a twofold approach. Formalising human expert knowledge about gold mining situations enables the retrieval of similar mining contexts and respective process chains, based on prospection data gathered from a potential gold mining site. Secondly, empirical knowledge on hydrometallurgical treatments is formalised. This enabled us to evaluate and, where needed, redesign the process chain that was recommended by the first aspect of our approach. The main problems with formalisation of knowledge in the domain of gold ore refinement are the diversity and the amount of parameters used in literature and by experts to describe a mining context. We demonstrate how similarity knowledge was used to formalise literature knowledge. The evaluation of data gathered from experiments with an initial prototype workflow recommender, Auric Adviser, provides promising results

Identifying and Evaluating Change Patterns and Change Support Features in Process-Aware Information Systems.

In order to provide effective support, the introduction of process-aware information systems (PAIS) must not freeze existing business processes. Instead PAIS should allow authorized users to flexibly deviate from the predefined processes if required and to evolve business processes in a controlled manner over time. Many software vendors promise flexible system solutions for realizing such adaptive PAIS, but are often unable to cope with fundamental issues elated to process change (e.g., correctness and robustness). The existence of different process support paradigms and the lack of methods for comparing existing change approaches makes it difficult for PAIS engineers to choose the adequate technology. In this paper we suggest a set of changes patterns and change support features to foster systematic comparison of existing process management technology with respect to change support. Based on these change patterns and features, we provide a detailed analysis and evaluation of selected systems from both academia and industry