Keeping the data lake in form: proximity mining for pre-filtering schema matching

Data Lakes (DLs) are large repositories of raw datasets from disparate sources. As more datasets are ingested into a DL, there is an increasing need for efficient techniques to profile them and to detect the relationships among their schemata, commonly known as holistic schema matching. Schema matching detects similarity between the information stored in the datasets to support information discovery and retrieval. Currently, this is computationally expensive with the volume of state-of-the-art DLs. To handle this challenge, we propose a novel early-pruning approach to improve efficiency, where we collect different types of content metadata and schema metadata about the datasets, and then use this metadata in early-pruning steps to pre-filter the schema matching comparisons. This involves computing proximities between datasets based on their metadata, discovering their relationships based on overall proximities and proposing similar dataset pairs for schema matching. We improve the effectiveness of this task by introducing a supervised mining approach for effectively detecting similar datasets which are proposed for further schema matching. We conduct extensive experiments on a real-world DL which proves the success of our approach in effectively detecting similar datasets for schema matching, with recall rates of more than 85% and efficiency improvements above 70%. We empirically show the computational cost saving in space and time by applying our approach in comparison to instance-based schema matching techniques.This research was partially funded by the European Commission through the Erasmus Mundus Joint Doctorate (IT4BI-DC).Peer ReviewedPostprint (author's final draft

Industrial Design of Electric Machines Supported with Knowledge-Based Engineering Systems

The demand for electric machines has increased in the last decade, mainly due to applications that try to make a full transition from fuel to electricity. These applications encounter the need for tailor-made electric machines that must meet demanding requirements. Therefore, it is necessary for small-medium companies to adopt new technologies offering customized products fulfilling the customers’ requirements according to their investment capacity, simplify their development process, and reduce computational time to achieve a feasible design in shorter periods. Furthermore, they must find ways to retain know-how that is typically kept within each designer to retrieve it or transfer it to new designers. This paper presents a framework with an implementation example of a knowledge-based engineering (KBE) system to design industrial electric machines to support this issue. The devised KBE system groups the main functionalities that provide the best outcome for an electric machine designer as development-process traceability, knowledge accessibility, automation of tasks, and intelligent support. The results show that if the company effectively applies these functionalities, they can leverage the attributes of KBE systems to shorten time-to-market. They can also ensure not losing all knowledge, information, and data through the whole development process

Reusability in manufacturing, supported by value net and patterns approaches

The concept of manufacturing and the need or desire to create artefacts or products is very, very old, yet it is still an essential component of all modem economies. Indeed, manufacturing is one of the few ways that wealth is created. The creation or identification of good quality, sustainable product designs is fundamental to the success of any manufacturing enterprise. Increasingly, there is also a requirement for the manufacturing system which will be used to manufacture the product, to be designed (or redesigned) in parallel with the product design. Many different types of manufacturing knowledge and information will contribute to these designs. A key question therefore for manufacturing companies to address is how to make the very best use of their existing, valuable, knowledge resources. […] The research reported in this thesis examines ways of reusing existing manufacturing knowledge of many types, particularly in the area of manufacturing systems design. The successes and failures of reported reuse programmes are examined, and lessons learnt from their experiences. This research is therefore focused on identifying solutions that address both technical and non-technical requirements simultaneously, to determine ways to facilitate and increase the reuse of manufacturing knowledge in manufacturing system design. [Continues.

Integrated Frameworks for Effective Multi-criteria Decision Making in Reliability Centred Maintenance of Industrial Machines

No abstract availabl

A new framework for supporting and managing multi-disciplinary system-simulation in a PLM environment

In order to keep products and systems attractive to consumers, developers have to do what they can to meet growing customers’ requirements. These requirements could be direct demands of customers but could also be the consequence of other influences such as globalization, customer fragmentation, product portfolio, regulations and so on. In the manufacturing industry, most companies are able to meet these growing requirements with mechatronic and interdisciplinary designed and developed products, which demand the collaboration between different disciplines. For example, the generation of a virtual prototype and its simulation tools of a mechatronic and multi-disciplinary product or system could require the cooperation of multiple departments within a company or between business partners. In a simulation, a virtual prototype is used for testing a product or a system. This virtual prototype and test approach could be used from the early stages of the development process to the end of the product or system lifecycle. Over years, different approaches/systems to generating virtual prototypes and testing have been designed and developed. But these systems have not been properly integrated, although some efforts have been made with limited success. Therefore, the requirement exists to propose and develop new technologies, methods and methodologies for achieving this integration.\ud In addition, the use of simulation tools requires special expertise for the generation of simulation models, plus the formats of product prototypes and simulation data are different for each system. This adds to the requirements of a guideline or framework for implementing the integration of a multi- and inter- disciplinary product design, simulation software and data management during the entire product lifecycle.\ud The main functionality and metadata structures of the new framework have been identified and optimised. The multi-disciplinary simulation data and their collection processes, the existing PLM (product lifecycle management) software and their applications have been analysed. In addition, the inter-disciplinary collaboration between a variety of simulation software has been analysed and evaluated. The new framework integrates the identified and optimised functionality and metadata structures to support and manage multi- and inter-disciplinary simulation in a PLM system environment.\ud It is believed that this project has made 6 contributions to new knowledge generation: (1) the New Conceptual Framework to Enhance the Support and Management of Multi-Disciplinary System-Simulation, (2) the New System-Simulation Oriented and Process Oriented Data Handling Approach, (3) the Enhanced Traceability of System-Simulation to Sources and Represented Products and Functions, (4) the New System-Simulation Derivation Approach, (5) the New Approach for the Synchronisation of System Describing Structures and (6) the Enhanced System-Simulation Result Data Handling Approach.\ud In addition, the new framework would bring significant benefits to each industry it is applied to. They are: (1) the more effective re-use of individual simulation models in system-simulation context, (2) the effective pre-defining and preparing of individual simulation models, (3) the easy and native reviewable system-simulation structures in relation to input-sources, such as products and / or functions, (4) the easy authoring-software independent update of system-simulation-structures, product-structures and function-structures, (5) the effective, distributed and cohesive post-process and interpretation of system-simulation-results, (6) the effective, easy and unique traceability of the data which means cost reductions in documentation and data security, and (7) the greater openness and flexibility in simulation software interactions with the data holding system.\ud Although the proposed and developed conceptual framework has not been implemented (that would require vast resources), it can be expected that the benefits in 7 above will lead to significant advances in the simulation of new product design and development over the whole lifecycle, offering enormous practical value to the manufacturing industry.\ud Due to time and resource constraints as well as the effort that would be involved in the implementation of the proposed new framework, it is clear there are some limitations to this PhD thesis. Five areas have been identified where further work is needed to improve the quality of this project: (1) an expanded industrial sector and product design and development processes, (2) parameter oriented system and production description in the new framework, (3) the improved user interface design of the new framework, (4) the automatic generation of simulation processes and (5) enhancement of the individual simulation models

The Role of preferences in logic programming: nonmonotonic reasoning, user preferences, decision under uncertainty

Intelligent systems that assist users in fulfilling complex tasks need a concise and processable representation of incomplete and uncertain information. In order to be able to choose among different options, these systems also need a compact and processable representation of the concept of preference. Preferences can provide an effective way to choose the best solutions to a given problem. These solutions can represent the most plausible states of the world when we model incomplete information, the most satisfactory states of the world when we express user preferences, or optimal decisions when we make decisions under uncertainty. Several domains, such as, reasoning under incomplete and uncertain information, user preference modeling, and qualitative decision making under uncertainty, have benefited from advances on preference representation. In the literature, several symbolic approaches of nonclassical reasoning have been proposed. Among them, logic programming under answer set semantics offers a good compromise between symbolic representation and computation of knowledge and several extensions for handling preferences. Nevertheless, there are still some open issues to be considered in logic programming. In nonmonotonic reasoning, first, most approaches assume that exceptions to logic program rules are already specified. However, sometimes, it is possible to consider implicit preferences based on the specificity of the rules to handle incomplete information. Secondly, the joint handling of exceptions and uncertainty has received little attention: when information is uncertain, the selection of default rules can be a matter of explicit preferences and uncertainty. In user preference modeling, although existing logic programming specifications allow to express user preferences which depend both on incomplete and contextual information, in some applications, some preferences in some context may be more important than others. Furthermore, more complex preference expressions need to be supported. In qualitative decision making under uncertainty, existing logic programming-based methodologies for making decisions seem to lack a satisfactory handling of preferences and uncertainty. The aim of this dissertation is twofold: 1) to tackle the role played by preferences in logic programming from different perspectives, and 2) to contribute to this novel field by proposing several frameworks and methods able to address the above issues. To this end, we will first show how preferences can be used to select default rules in logic programs in an implicit and explicit way. In particular, we propose (i) a method for selecting logic program rules based on specificity, and (ii) a framework for selecting uncertain default rules based on explicit preferences and the certainty of the rules. Then, we will see how user preferences can be modeled and processed in terms of a logic program (iii) in order to manage user profiles in a context-aware system and (iv) in order to propose a framework for the specification of nested (non-flat) preference expressions. Finally, in the attempt to bridge the gap between logic programming and qualitative decision under uncertainty, (v) we propose a classical- and a possibilistic-based logic programming methodology to compute an optimal decision when uncertainty and preferences are matters of degrees.Els sistemes intel.ligents que assisteixen a usuaris en la realització de tasques complexes necessiten una representació concisa i formal de la informació que permeti un raonament nomonòton en condicions d’incertesa. Per a poder escollir entre les diferents opcions, aquests sistemes solen necessitar una representació del concepte de preferència. Les preferències poden proporcionar una manera efectiva de triar entre les millors solucions a un problema. Aquestes solucions poden representar els estats del món més plausibles quan es tracta de modelar informació incompleta, els estats del món més satisfactori quan expressem preferències de l’usuari, o decisions òptimes quan estem parlant de presa de decisió incorporant incertesa. L’ús de les preferències ha beneficiat diferents dominis, com, el raonament en presència d’informació incompleta i incerta, el modelat de preferències d’usuari, i la presa de decisió sota incertesa. En la literatura, s’hi troben diferents aproximacions al raonament no clàssic basades en una representació simbòlica de la informació. Entre elles, l’enfocament de programació lògica, utilitzant la semàntica de answer set, ofereix una bona aproximació entre representació i processament simbòlic del coneixement, i diferents extensions per gestionar les preferències. No obstant això, en programació lògica es poden identificar diferents problemes pel que fa a la gestió de les preferències. Per exemple, en la majoria d’enfocaments de raonament no-monòton s’assumeix que les excepcions a default rules d’un programa lògic ja estan expressades. Però de vegades es poden considerar preferències implícites basades en l’especificitat de les regles per gestionar la informació incompleta. A més, quan la informació és també incerta, la selecció de default rules pot dependre de preferències explícites i de la incertesa. En el modelatge de preferències del usuari, encara que els formalismes existents basats en programació lògica permetin expressar preferències que depenen d’informació contextual i incompleta, en algunes aplicacions, donat un context, algunes preferències poden ser més importants que unes altres. Per tant, resulta d’interès un llenguatge que permeti capturar preferències més complexes. En la presa de decisions sota incertesa, les metodologies basades en programació lògica creades fins ara no ofereixen una solució del tot satisfactòria pel que fa a la gestió de les preferències i la incertesa. L’objectiu d’aquesta tesi és doble: 1) estudiar el paper de les preferències en la programació lògica des de diferents perspectives, i 2) contribuir a aquesta jove àrea d’investigació proposant diferents marcs teòrics i mètodes per abordar els problemes anteriorment citats. Per a aquest propòsit veurem com les preferències es poden utilitzar de manera implícita i explícita per a la selecció de default rules proposant: (i) un mètode basat en l’especificitat de les regles, que permeti seleccionar regles en un programa lògic; (ii) un marc teòric per a la selecció de default rules incertes basat en preferències explícites i la incertesa de les regles. També veurem com les preferències de l’usuari poden ser modelades i processades usant un enfocament de programació lògica (iii) que suporti la creació d’un mecanisme de gestió dels perfils dels usuaris en un sistema amb reconeixement del context; (iv) que permeti proposar un marc teòric capaç d’expressar preferències amb fòrmules imbricades. Per últim, amb l’objectiu de disminuir la distància entre programació lògica i la presa de decisió amb incertesa proposem (v) una metodologia basada en programació lògica clàssica i en una extensió de la programació lògica que incorpora lògica possibilística per modelar un problema de presa de decisions i per inferir una decisió òptima.Los sistemas inteligentes que asisten a usuarios en tareas complejas necesitan una representación concisa y procesable de la información que permita un razonamiento nomonótono e incierto. Para poder escoger entre las diferentes opciones, estos sistemas suelen necesitar una representación del concepto de preferencia. Las preferencias pueden proporcionar una manera efectiva para elegir entre las mejores soluciones a un problema. Dichas soluciones pueden representar los estados del mundo más plausibles cuando hablamos de representación de información incompleta, los estados del mundo más satisfactorios cuando hablamos de preferencias del usuario, o decisiones óptimas cuando estamos hablando de toma de decisión con incertidumbre. El uso de las preferencias ha beneficiado diferentes dominios, como, razonamiento en presencia de información incompleta e incierta, modelado de preferencias de usuario, y toma de decisión con incertidumbre. En la literatura, distintos enfoques simbólicos de razonamiento no clásico han sido creados. Entre ellos, la programación lógica con la semántica de answer set ofrece un buen acercamiento entre representación y procesamiento simbólico del conocimiento, y diferentes extensiones para manejar las preferencias. Sin embargo, en programación lógica se pueden identificar diferentes problemas con respecto al manejo de las preferencias. Por ejemplo, en la mayoría de enfoques de razonamiento no-monótono se asume que las excepciones a default rules de un programa lógico ya están expresadas. Pero, a veces se pueden considerar preferencias implícitas basadas en la especificidad de las reglas para manejar la información incompleta. Además, cuando la información es también incierta, la selección de default rules pueden depender de preferencias explícitas y de la incertidumbre. En el modelado de preferencias, aunque los formalismos existentes basados en programación lógica permitan expresar preferencias que dependen de información contextual e incompleta, in algunas aplicaciones, algunas preferencias en un contexto puede ser más importantes que otras. Por lo tanto, un lenguaje que permita capturar preferencias más complejas es deseable. En la toma de decisiones con incertidumbre, las metodologías basadas en programación lógica creadas hasta ahora no ofrecen una solución del todo satisfactoria al manejo de las preferencias y la incertidumbre. El objectivo de esta tesis es doble: 1) estudiar el rol de las preferencias en programación lógica desde diferentes perspectivas, y 2) contribuir a esta joven área de investigación proponiendo diferentes marcos teóricos y métodos para abordar los problemas anteriormente citados. Para este propósito veremos como las preferencias pueden ser usadas de manera implícita y explícita para la selección de default rules proponiendo: (i) un método para seleccionar reglas en un programa basado en la especificad de las reglas; (ii) un marco teórico para la selección de default rules basado en preferencias explícitas y incertidumbre. También veremos como las preferencias del usuario pueden ser modeladas y procesadas usando un enfoque de programación lógica (iii) para crear un mecanismo de manejo de los perfiles de los usuarios en un sistema con reconocimiento del contexto; (iv) para crear un marco teórico capaz de expresar preferencias con formulas anidadas. Por último, con el objetivo de disminuir la distancia entre programación lógica y la toma de decisión con incertidumbre proponemos (v) una metodología para modelar un problema de toma de decisiones y para inferir una decisión óptima usando un enfoque de programación lógica clásica y uno de programación lógica extendida con lógica posibilística.Sistemi intelligenti, destinati a fornire supporto agli utenti in processi decisionali complessi, richiedono una rappresentazione dell’informazione concisa, formale e che permetta di ragionare in maniera non monotona e incerta. Per poter scegliere tra le diverse opzioni, tali sistemi hanno bisogno di disporre di una rappresentazione del concetto di preferenza altrettanto concisa e formale. Le preferenze offrono una maniera efficace per scegliere le miglior soluzioni di un problema. Tali soluzioni possono rappresentare gli stati del mondo più credibili quando si tratta di ragionamento non monotono, gli stati del mondo più soddisfacenti quando si tratta delle preferenze degli utenti, o le decisioni migliori quando prendiamo una decisione in condizioni di incertezza. Diversi domini come ad esempio il ragionamento non monotono e incerto, la strutturazione del profilo utente, e i modelli di decisione in condizioni d’incertezza hanno tratto beneficio dalla rappresentazione delle preferenze. Nella bibliografia disponibile si possono incontrare diversi approcci simbolici al ragionamento non classico. Tra questi, la programmazione logica con answer set semantics offre un buon compromesso tra rappresentazione simbolica e processamento dell’informazione, e diversi estensioni per la gestione delle preferenze sono state proposti in tal senso. Nonostante ció, nella programmazione logica esistono ancora delle problematiche aperte. Prima di tutto, nella maggior parte degli approcci al ragionamento non monotono, si suppone che nel programma le eccezioni alle regole siano già specificate. Tuttavia, a volte per trattare l’informazione incompleta è possibile prendere in considerazione preferenze implicite basate sulla specificità delle regole. In secondo luogo, la gestione congiunta di eccezioni e incertezza ha avuto scarsa attenzione: quando l’informazione è incerta, la scelta di default rule può essere una questione di preferenze esplicite e d’incertezza allo stesso tempo. Nella creazione di preferenze dell’utente, anche se le specifiche di programmazione logica esistenti permettono di esprimere preferenze che dipendono sia da un’informazione incompleta che da una contestuale, in alcune applicazioni talune preferenze possono essere più importanti di altre, o espressioni più complesse devono essere supportate. In un processo decisionale con incertezza, le metodologie basate sulla programmazione logica viste sinora, non offrono una gestione soddisfacente delle preferenze e dell’incertezza. Lo scopo di questa dissertazione è doppio: 1) chiarire il ruolo che le preferenze giocano nella programmazione logica da diverse prospettive e 2) contribuire proponendo in questo nuovo settore di ricerca, diversi framework e metodi in grado di affrontare le citate problematiche. Per prima cosa, dimostreremo come le preferenze possono essere usate per selezionare default rule in un programma in maniera implicita ed esplicita. In particolare proporremo: (i) un metodo per la selezione delle regole di un programma logico basato sulla specificità dell’informazione; (ii) un framework per la selezione di default rule basato sulle preferenze esplicite e sull’incertezza associata alle regole del programma. Poi, vedremo come le preferenze degli utenti possono essere modellate attraverso un programma logico, (iii) per creare il profilo dell’utente in un sistema context-aware, e (iv) per proporre un framework che supporti la definizione di preferenze complesse. Infine, per colmare le lacune in programmazione logica applicata a un processo di decisione con incertezza (v) proporremo una metodologia basata sulla programmazione logica classica e una metodologia basata su un’estensione della programmazione logica con logica possibilistica