A mereotopological product relationship description approach for assembly oriented design

This paper describes a novel approach for integrated assembly modelling and planning. The main objective is to make assembly information accessible and exploitable by data management systems and computer-aided X tools in order to support product architects and designers. Product information and knowledge as well as the related assembly sequence require a logical foundation in order to be managed consistently and processed proactively. In this context, product relationships are considered and described in the part-whole theory supported by mereology and its extension, mereotopology. Firstly, past and current research work are presented on concurrent product design and assembly sequence planning approaches; existing assembly relational models; and spatio-temporal mereotopology. A background of previous research work is also included in order to highlight the current research problem. Then, a mathematical description approach of product relationships based on mereotopology and temporal relationships is introduced. Finally, an ontological implementation of the proposed description using OWL DL and SWRL is presented and illustrated in a case study describing a mechanical assembly, enabling hence, reuse and collaborative exploitation of the assembly knowledge in the different product lifecycle phases. (C) 2012 Elsevier Ltd. All rights reserved.

Adaptive Query Processing on Raw Data Files

Ioannis Alagiannis

Nowadays, business and scientific applications accumulate data at an increasing pace. This growth of information has already started to outgrow the capabilities of database management systems (DBMS). In a typical DBMS usage scenario, the user should define a schema, load the data and tune the system for an expected workload before submitting any queries. Copying data into a database is a significant investment in terms of time and resources, and in many cases unnecessary or even no longer feasible in practice due to the explosive data growth. Additionally, the way DBMS store and organize data during data loading defines how data will be accessed for a given workload and thus, the maximum performance. Selecting the underlying data layout (row-store or column-store) is a critical first tuning decision which cannot change. Nevertheless, today query analysis is not static; it evolves as queries change. Hence, static design decisions can be suboptimal. In this thesis, we advocate in situ query processing as the principal way to manage data in a database. We reconsider the data loading phase and redesign traditional query processing architectures to work efficiently over raw data files to address the heavy initialization cost that comes with data loading. We present adaptive data loading as an alternative to traditional full a priori data loading. We explore the potential of in situ query processing in the context of current DBMS architectures. We identify performance bottlenecks specific for in situ processing and we introduce an adaptive indexing mechanism (positional map) that maintains positional information to provide efficient access to raw data files, together with a flexible caching structure and techniques for collecting statistics over raw data files. Moreover, we design a flexible query engine that is not built around a single storage layout but it can exploit different storage layouts and data execution strategies in a single engine. It decides during query processing, which design fits the input queries and properly adapts the underlying data storage. By applying code generation techniques, we dynamically generate access operators tailored for specific classes of queries. This thesis revises the traditional paradigm of loading, tuning and then querying by using in situ query processing as the principal way to minimize data-to-query time. We show that raw data files should not be considered ``outside'' the DBMS and full data loading should not be a requirement to exploit database technology. On the contrary, proper techniques specifically tailored to overcome limitations that come with accessing raw data files can eliminate the data loading overhead making, therefore, raw data files a first-class citizen, fully integrated with the query engine. The proposed roadmap can provide guidance on how to convert any traditional DBMS into an efficient in situ query engine.

EPFL2015

Towards a pre-design method for low carbon architectural strategies

Marilyne Andersen, Arianna Brambilla, Stefano Cozza, Endrit Hoxha, Thomas Bernard Paul Jusselme

To face climate change, Switzerland proposes the 2050 energy strategy by fixing greenhouse gas (GHG) emission targets for the built environment. Designers will then have to increase operating performances while inimizing embodied impacts. This represents an issue for the building design process. In addition, there is a relationship between the design efficiency and the early integration of the knowledge about design. The purpose of this paper is to highlight the potential of a pre-design method to identify the building design parameters that reach the 2050 climate change objectives. To that end, four major steps are developed in this project. First, design parameters (e.g. wall thermal transmittance) which influence the building GHG emissions the most, are identified thanks to a literature review. Morris method (Saltelli et al, 2004) is used to create combinations of design parameters changing their values one by one. Secondly, these combinations are attributed to architectural feasibility studies (Sinclair, 2013) developed in the brief design phase to perform lifecycle analysis. Thirdly, KBOB database (KBOB et al., 2014) and lifetime of components proposed by PI-BAT were used for assessing GHG emissions. Lesosai software was used for primary energy assessment. Lastly, the combinations of design parameters and their relative GHG emissions are interpreted with data mining and visualization techniques. The smart living lab building has been chosen as a case study: this building aims at achieving the 2050 goals of the 2000-watt society vision and will be built by 2020 in Fribourg, Switzerland. Thanks to the preliminary results it is possible to rank the design parameters according to their GHG contribution, in order to highlight them during the early building design stage. The method offers combinations of design parameters allowing to reach the 2050 climate change objectives. Data mining and visualization enable designers to easily find the values of these parameters to fit into the architectural strategy. In order to offer a wider range of design parameter values, techniques to enhance the database should be further investigated.

2016

Adaptive Query Processing on Raw Data Files

Ioannis Alagiannis

EPFL2015

Towards a pre-design method for low carbon architectural strategies

Marilyne Andersen, Arianna Brambilla, Stefano Cozza, Endrit Hoxha, Thomas Bernard Paul Jusselme

2016