18,551 research outputs found
On the role of pre and post-processing in environmental data mining
The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed
Using spatiotemporal patterns to qualitatively represent and manage dynamic situations of interest : a cognitive and integrative approach
Les situations spatio-temporelles dynamiques sont des situations qui Ă©voluent dans lâespace et dans le temps. LâĂȘtre humain peut identifier des configurations de situations dans son environnement et les utilise pour prendre des dĂ©cisions. Ces configurations de situations peuvent aussi ĂȘtre appelĂ©es « situations dâintĂ©rĂȘt » ou encore « patrons spatio-temporels ». En informatique, les situations sont obtenues par des systĂšmes dâacquisition de donnĂ©es souvent prĂ©sents dans diverses industries grĂące aux rĂ©cents dĂ©veloppements technologiques et qui gĂ©nĂšrent des bases de donnĂ©es de plus en plus volumineuses. On relĂšve un problĂšme important dans la littĂ©rature liĂ© au fait que les formalismes de reprĂ©sentation utilisĂ©s sont souvent incapables de reprĂ©senter des phĂ©nomĂšnes spatiotemporels dynamiques et complexes qui reflĂštent la rĂ©alitĂ©. De plus, ils ne prennent pas en considĂ©ration lâapprĂ©hension cognitive (modĂšle mental) que lâhumain peut avoir de son environnement. Ces facteurs rendent difficile la mise en Ćuvre de tels modĂšles par des agents logiciels. Dans cette thĂšse, nous proposons un nouveau modĂšle de reprĂ©sentation des situations dâintĂ©rĂȘt sâappuyant sur la notion des patrons spatiotemporels. Notre approche utilise les graphes conceptuels pour offrir un aspect qualitatif au modĂšle de reprĂ©sentation. Le modĂšle se base sur les notions dâĂ©vĂ©nement et dâĂ©tat pour reprĂ©senter des phĂ©nomĂšnes spatiotemporels dynamiques. Il intĂšgre la notion de contexte pour permettre aux agents logiciels de raisonner avec les instances de patrons dĂ©tectĂ©s. Nous proposons aussi un outil de gĂ©nĂ©ration automatisĂ©e des relations qualitatives de proximitĂ© spatiale en utilisant un classificateur flou. Finalement, nous proposons une plateforme de gestion des patrons spatiotemporels pour faciliter lâintĂ©gration de notre modĂšle dans des applications industrielles rĂ©elles. Ainsi, les contributions principales de notre travail sont : Un formalisme de reprĂ©sentation qualitative des situations spatiotemporelles dynamiques en utilisant des graphes conceptuels. ; Une approche cognitive pour la dĂ©finition des patrons spatio-temporels basĂ©e sur lâintĂ©gration de lâinformation contextuelle. ; Un outil de gĂ©nĂ©ration automatique des relations spatiales qualitatives de proximitĂ© basĂ© sur les classificateurs neuronaux flous. ; Une plateforme de gestion et de dĂ©tection des patrons spatiotemporels basĂ©e sur lâextension dâun moteur de traitement des Ă©vĂ©nements complexes (Complex Event Processing).Dynamic spatiotemporal situations are situations that evolve in space and time. They are part of humansâ daily life. One can be interested in a configuration of situations occurred in the environment and can use it to make decisions. In the literature, such configurations are referred to as âsituations of interestsâ or âspatiotemporal patternsâ. In Computer Science, dynamic situations are generated by large scale data acquisition systems which are deployed everywhere thanks to recent technological advances. Spatiotemporal pattern representation is a research subject which gained a lot of attraction from two main research areas. In spatiotemporal analysis, various works extended query languages to represent patterns and to query them from voluminous databases. In Artificial Intelligence, predicate-based models represent spatiotemporal patterns and detect their instances using rule-based mechanisms. Both approaches suffer several shortcomings. For example, they do not allow for representing dynamic and complex spatiotemporal phenomena due to their limited expressiveness. Furthermore, they do not take into account the humanâs mental model of the environment in their representation formalisms. This limits the potential of building agent-based solutions to reason about these patterns. In this thesis, we propose a novel approach to represent situations of interest using the concept of spatiotemporal patterns. We use Conceptual Graphs to offer a qualitative representation model of these patterns. Our model is based on the concepts of spatiotemporal events and states to represent dynamic spatiotemporal phenomena. It also incorporates contextual information in order to facilitate building the knowledge base of software agents. Besides, we propose an intelligent proximity tool based on a neuro-fuzzy classifier to support qualitative spatial relations in the pattern model. Finally, we propose a framework to manage spatiotemporal patterns in order to facilitate the integration of our pattern representation model to existing applications in the industry. The main contributions of this thesis are as follows: A qualitative approach to model dynamic spatiotemporal situations of interest using Conceptual Graphs. ; A cognitive approach to represent spatiotemporal patterns by integrating contextual information. ; An automated tool to generate qualitative spatial proximity relations based on a neuro-fuzzy classifier. ; A platform for detection and management of spatiotemporal patterns using an extension of a Complex Event Processing engine
Combining ontological and temporal formalisms for composite activity modelling and recognition in smart homes
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Activity recognition is essential in providing activity assistance for users in smart homes. While significant progress has been made for single-user single-activity recognition, it still remains a challenge to carry out real-time progressive composite activity recognition. This paper introduces a hybrid ontological and temporal approach to composite activity modelling and recognition by extending existing ontology-based knowledge-driven approach. The compelling feature of the approach is that it combines ontological and temporal knowledge representation formalisms to provide powerful representation capabilities for activity modelling. The paper describes in detail ontological activity modelling which establishes relationships between activities and their involved entities, and temporal activity modelling which defines relationships between constituent activities of a composite activity. As an essential part of the model, the paper also presents methods for developing temporal entailment rules to support the interpretation and inference of composite activities. In addition, this paper outlines an integrated architecture for composite activity recognition and elaborated a unified activity recognition algorithm which can support the recognition of simple and composite activities. The approach has been implemented in a feature-rich prototype system upon which testing and evaluation have been conducted. Initial experimental results have shown average recognition accuracy of 100% and 88.26% for simple and composite activities, respectively
Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective
Data-driven decision making is becoming an integral part of manufacturing
companies. Data is collected and commonly used to improve efficiency and
produce high quality items for the customers. IoT-based and other forms of
object tracking are an emerging tool for collecting movement data of
objects/entities (e.g. human workers, moving vehicles, trolleys etc.) over
space and time. Movement data can provide valuable insights like process
bottlenecks, resource utilization, effective working time etc. that can be used
for decision making and improving efficiency.
Turning movement data into valuable information for industrial management and
decision making requires analysis methods. We refer to this process as movement
analytics. The purpose of this document is to review the current state of work
for movement analytics both in manufacturing and more broadly.
We survey relevant work from both a theoretical perspective and an
application perspective. From the theoretical perspective, we put an emphasis
on useful methods from two research areas: machine learning, and logic-based
knowledge representation. We also review their combinations in view of movement
analytics, and we discuss promising areas for future development and
application. Furthermore, we touch on constraint optimization.
From an application perspective, we review applications of these methods to
movement analytics in a general sense and across various industries. We also
describe currently available commercial off-the-shelf products for tracking in
manufacturing, and we overview main concepts of digital twins and their
applications
Modeling of Phenomena and Dynamic Logic of Phenomena
Modeling of complex phenomena such as the mind presents tremendous
computational complexity challenges. Modeling field theory (MFT) addresses
these challenges in a non-traditional way. The main idea behind MFT is to match
levels of uncertainty of the model (also, problem or theory) with levels of
uncertainty of the evaluation criterion used to identify that model. When a
model becomes more certain, then the evaluation criterion is adjusted
dynamically to match that change to the model. This process is called the
Dynamic Logic of Phenomena (DLP) for model construction and it mimics processes
of the mind and natural evolution. This paper provides a formal description of
DLP by specifying its syntax, semantics, and reasoning system. We also outline
links between DLP and other logical approaches. Computational complexity issues
that motivate this work are presented using an example of polynomial models
Big data analytics:Computational intelligence techniques and application areas
Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment
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