Querying a regulatory model for compliant building design audit

The ingredients for an effective automated audit of a building design include a BIM model containing the design information, an electronic regulatory knowledge model, and a practical method of processing these computerised representations. There have been numerous approaches to computer-aided compliance audit in the AEC/FM domain over the last four decades, but none has yet evolved into a practical solution. One reason is that they have all been isolated attempts that lack any form of standardisation. The current research project therefore focuses on using an open standard regulatory knowledge and BIM representations in conjunction with open standard executable compliant design workflows to automate the compliance audit process. This paper provides an overview of different approaches to access information from a regulatory model representation. The paper then describes the use of a purpose-built high-level domain specific query language to extract regulatory information as part of the effort to automate manual design procedures for compliance audit

Data queries over heterogeneous sources

Dissertação para obtenção do Grau de Mestre em Engenharia InformáticaEnterprises typically have their data spread over many software systems, such as custom made applications, CRM systems like SalesForce, CMS systems, or ERP systems like SAP. In these setting, it is often desired to integrate information from many data sources to accomplish some business goal in an application. Data may be stored locally or in the cloud in a wide variety of ways, demanding for explicit transformation processes to be defined, reason why it is hard for developers to integrate it. Moreover, the amount of external data can be large and the difference of efficiency between a smart and a naive way of retrieving and filtering data from different locations can be great. Hence, it is clear that developers would benefit greatly from language abstractions to help them build queries over heterogeneous data sources and from an optimization process that avoids large and unnecessary data transfers during the execution of queries. This project was developed at OutSystems and aims at extending a real product, which makes it even more challenging. We followed a generic approach that can be implemented in any framework, not only focused on the product of OutSystems

Towards a Framework for a Domain Specific Open Query Language for Building Information Models

In this paper we present the on-going development of a framework for a Domain Specific Open Query Language for Building Information Models. This query language will make it possible to retrieve data from building information models stored on the open source bimserver.org model server. Even though some partial solutions to this problem already have been suggested, none of them are open source, domain specific, platform independent and implemented at the same time. This paper provides an overview of existing approaches and conceptual sketches of the language in development

Comparative analysis of the applicability of BIM query languages for energy analysis

Paper no. 036A range of query languages have been used or developed to query partial information from Building Information Model (BIM)-based databases and files in recent decades. This paper aims to investigate the applicability of existing BIM query languages to extract necessary information from BIMs for energy analysis. A total of 16 query languages categorized into two groups, namely programming or generic query language, and domain specific query language, are summarized through extensive literature review. The key requirements of BIM data query for energy analysis are also developed, which include MVD based query support, custom query support, and easiness to construct queries. Taking these requirements as the criteria, the applicability of the 16 query languages is compared and analyzed. This paper then proposes some suggestions for developing effective and efficient building information query mechanisms for energy analysis.postprin

The Family of MapReduce and Large Scale Data Processing Systems

In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data which has called for a paradigm shift in the computing architecture and large scale data processing mechanisms. MapReduce is a simple and powerful programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. It isolates the application from the details of running a distributed program such as issues on data distribution, scheduling and fault tolerance. However, the original implementation of the MapReduce framework had some limitations that have been tackled by many research efforts in several followup works after its introduction. This article provides a comprehensive survey for a family of approaches and mechanisms of large scale data processing mechanisms that have been implemented based on the original idea of the MapReduce framework and are currently gaining a lot of momentum in both research and industrial communities. We also cover a set of introduced systems that have been implemented to provide declarative programming interfaces on top of the MapReduce framework. In addition, we review several large scale data processing systems that resemble some of the ideas of the MapReduce framework for different purposes and application scenarios. Finally, we discuss some of the future research directions for implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author

Semantos : a semantically smart information query language

Enterprise Information Integration (EII) is rapidly becoming one of the pillars of modern corporate information systems. Given the spread and diversity of information sources in an enterprise, it has become increasingly difficult for decision makers to have access to relevant and accurate information at the opportune time. It has therefore become critical to seamlessly integrate the diverse information stores found in an organization into a single coherent data source. This is the job of EII and one of the key components to making it work is harnessing the implied meaning or semantics hidden within data sources. Modern EII systems are capable of harnessing semantic information and ontologies to make integration across data stores possible. These systems do not, however, allow a consumer of the integration service to build queries with semantic meaning. This is due to the fact that most EII systems make use of XQuery, SQL, or both, as query languages, neither of which has the capability to build semantically rich queries. In this thesis Semantos (from the Greek word sema for “sign or token”) is proposed as a viable alternative: an information query language based in XML, which is capable of exploiting ontologies, enabling consumers to build semantically enriched queries. An exploration is made into the characteristics or requirements that Semantos needs to satisfy as a semantically smart information query language. From these requirements we design and develop a software implementation. The benefit of Semantos is that it possesses a query structure that allows automated processes to decompose and restructure the queries without human intervention. We demonstrate the applicability of Semantos using two realistic examples: a query enhancement- and a query translation service. Both expound the ability of a Semantos query to be manipulated by automated services to achieve Information Integration goals.Dissertation (MSc)--University of Pretoria, 2009.Computer Scienceunrestricte