Design and Implementation of a Multi-Purpose Object-Orientated Spatio-Temporal (MPooST) Data Model for Cadastral and Land Information Systems (C/LIS)

The application of the object-oriented methodology in geospatial information management has significantly increased during the last 10 years and tends to gradually replace the status quo relational technology. In general, object orientation offers a flexible and adaptable modelling framework to satisfy the most demanding complex data structuring requirements. The objective of this thesis is to determine how a modern Land Information System used for cadastral purposes can benefit from an object-oriented methodology. To this aim, a Multi-Purpose, Object-Oriented Spatio-Temporal (abbreviated as MPOOST) data model has been developed. In brief, the MPOOST data model embodies spatial data and their temporal reference in the form of objects which contain their attributes as well as their behaviour. The design of the MPOOST data model has been specified in such a way that it enables other data models to exploit its functionality, therefore enabling the multi-purpose aspect. At first, the requirements of Land Information Systems are being examined. Next, the functionality that is offered by the object-oriented methodology is being analysed in detail. Even if the bibliography is quite rich in relevant research, however there seems to be no starting point regarding the application of OO in LIS. Hence, a whole chapter of this thesis has been dedicated in an extended bibliographic research. Finally, the OO methodology is applied for the design and implementation of the MPOOST data model. The outcome of the design and the implementation is the first version of the MPOOST data model written using the Java object-oriented programming language. In this way, it is proven that: the relational technology has significant drawbacks which prohibit it from being applied in conceptually demanding information systems; and that object-orientation can fully satisfy the most complex data structuring requirements posed in modern geographic information systems

Object-based modelling for representing and processing speech corpora

This thesis deals with modelling data existing in large speech corpora using an object-oriented paradigm which captures important linguistic structures. Information from corpora is transformed into objects and are assigned properties regarding their behaviour. These objects, called speech units, are placed onto a multi-dimensional framework and have their relationships to other units explicitly defined through the use of links. Frameworks that model temporal utterances or atemporal information like speaker characteristics and recording conditions can be searched efficiently for contextual matches. Speech units that match desired contexts are the result of successful linguistically motivated queries and can be used in further speech processing tasks in the same computational environment. This allows for empirical studies of speech and its relation to linguistic structures to be carried out, and for the training and testing of applications like speech recognition and synthesis. Information residing in typical speech corpora is discussed first, followed by an overview of object-orientation which sets the tone for this thesis. Then the representation framework is introduced which is generated by a compiler and linker that rely on a set of domain-specific resources that transform corpus data into speech units. Operations on this framework are then presented along with a comparison between a relational and object-oriented model of identical speech data. The models described in this work are directly applicable to existing large speech corpora, and the methods developed here are tested against relational database methods. The object-oriented methods outperform the relational methods for typical linguistically relevant queries by about three orders of magnitude as measured by database search times. This improvement in simplicity of representation and search speed is crucial for the utilisation of large multi-lingual corpora in basic research on the detailed properties of speech, especially in relation to contextual variation.reviewe

Object-Oriented Dynamics Learning through Multi-Level Abstraction

Object-based approaches for learning action-conditioned dynamics has demonstrated promise for generalization and interpretability. However, existing approaches suffer from structural limitations and optimization difficulties for common environments with multiple dynamic objects. In this paper, we present a novel self-supervised learning framework, called Multi-level Abstraction Object-oriented Predictor (MAOP), which employs a three-level learning architecture that enables efficient object-based dynamics learning from raw visual observations. We also design a spatial-temporal relational reasoning mechanism for MAOP to support instance-level dynamics learning and handle partial observability. Our results show that MAOP significantly outperforms previous methods in terms of sample efficiency and generalization over novel environments for learning environment models. We also demonstrate that learned dynamics models enable efficient planning in unseen environments, comparable to true environment models. In addition, MAOP learns semantically and visually interpretable disentangled representations.Comment: Accepted to the Thirthy-Fourth AAAI Conference On Artificial Intelligence (AAAI), 202

A Semantic-based framework for discovering business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modeling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. This paper focuses on business process patterns and proposes an initial framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework synthesizes the idea from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

TEMPOS: A Platform for Developing Temporal Applications on Top of Object DBMS

This paper presents TEMPOS: a set of models and languages supporting the manipulation of temporal data on top of object DBMS. The proposed models exploit object-oriented technology to meet some important, yet traditionally neglected design criteria related to legacy code migration and representation independence. Two complementary ways for accessing temporal data are offered: a query language and a visual browser. The query language, namely TempOQL, is an extension of OQL supporting the manipulation of histories regardless of their representations, through fully composable functional operators. The visual browser offers operators that facilitate several time-related interactive navigation tasks, such as studying a snapshot of a collection of objects at a given instant, or detecting and examining changes within temporal attributes and relationships. TEMPOS models and languages have been formalized both at the syntactical and the semantical level and have been implemented on top of an object DBMS. The suitability of the proposals with regard to applications' requirements has been validated through concrete case studies

A logic programming framework for modeling temporal objects

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