Semantic process mining tools: core building blocks

Process mining aims at discovering new knowledge based on information hidden in event logs. Two important enablers for such analysis are powerful process mining techniques and the omnipresence of event logs in today's information systems. Most information systems supporting (structured) business processes (e.g. ERP, CRM, and workflow systems) record events in some form (e.g. transaction logs, audit trails, and database tables). Process mining techniques use event logs for all kinds of analysis, e.g., auditing, performance analysis, process discovery, etc. Although current process mining techniques/tools are quite mature, the analysis they support is somewhat limited because it is purely based on labels in logs. This means that these techniques cannot benefit from the actual semantics behind these labels which could cater for more accurate and robust analysis techniques. Existing analysis techniques are purely syntax oriented, i.e., much time is spent on filtering, translating, interpreting, and modifying event logs given a particular question. This paper presents the core building blocks necessary to enable semantic process mining techniques/tools. Although the approach is highly generic, we focus on a particular process mining technique and show how this technique can be extended and implemented in the ProM framework tool

Building an application for the writing process

The idea that writing is a process and not a product is now generally accepted in writing education, but discussions of digital scholarly communication often neglect the idea, in theory and in practice. This thesis report introduces a Mac OS X software package to support the early stages of the writing process, called Brouillon. Brouillon’s features include: the concatenation of discrete note files into notebooks; notes appearing in multiple notebooks; note intake from mobile devices via Dropbox; and an open standard file format. The report also provides a model of the organization of products of the writing process, with a focus on Brouillon’s most unusual feature, multi-notebook notes. It discusses difficulties in implementation and identifies possibilities for future improvement

How Do I Build Green?

Covers the process of an integrated and collaborative approach to the green building process. Part of Kresge's Green Building Initiative

Role expectation process in building a desirable work culture

Includes bibliographical references

Application of BIM in sustainability analysis

Building Information Modeling (BIM) is the process of generating and managing building data during its life cycle. Typically it uses three-dimensional, real-time, dynamic building modeling software to increase productivity in building design and construction. The process produces the Building Information Model, which encompasses building geometry, spatial relationships, geographic information, quantities and properties of building components. On the other hand, Green Building Index (GBI) as the localized sustainability building rating system in Malaysia assesses the impact of building on environment based on energy efficiency, indoor environment quality, sustainable site & management, materials & resources, water efficiency and innovation. By integrating GBI assessment criteria with BIM technology, this research proposes a comparative case study analysis of Residential New Construction (RNC) and Non-Residential New Construction (NRNC) based on the Autodesk Ecotect Analysis software (a comprehensive, concept-to-detail sustainable design analysis tool that provides a wide range of simulation and analysis functionality through desktop and web-service platforms) and Autodesk Green Building Studio (A web-based energy analysis service which performs whole building analysis, optimizes energy efficiency, and works toward carbon neutrality earlier in the design process) to investigate the influence of construction material on energy consumption, lifecycle energy cost and carbon emission

Consensus-building process

(4pp.

Building nonparametric nn-body force fields using Gaussian process regression

Constructing a classical potential suited to simulate a given atomic system is a remarkably difficult task. This chapter presents a framework under which this problem can be tackled, based on the Bayesian construction of nonparametric force fields of a given order using Gaussian process (GP) priors. The formalism of GP regression is first reviewed, particularly in relation to its application in learning local atomic energies and forces. For accurate regression it is fundamental to incorporate prior knowledge into the GP kernel function. To this end, this chapter details how properties of smoothness, invariance and interaction order of a force field can be encoded into corresponding kernel properties. A range of kernels is then proposed, possessing all the required properties and an adjustable parameter $n$ governing the interaction order modelled. The order $n$ best suited to describe a given system can be found automatically within the Bayesian framework by maximisation of the marginal likelihood. The procedure is first tested on a toy model of known interaction and later applied to two real materials described at the DFT level of accuracy. The models automatically selected for the two materials were found to be in agreement with physical intuition. More in general, it was found that lower order (simpler) models should be chosen when the data are not sufficient to resolve more complex interactions. Low $n$ GPs can be further sped up by orders of magnitude by constructing the corresponding tabulated force field, here named "MFF".Comment: 31 pages, 11 figures, book chapte