1,721,144 research outputs found
An intensional approach for periodic data in relational databases
No full textPeriodic data play a major role in many application domains, spanning from manufacturing to office automation, from scheduling to data broadcasting. In many of such domains, the huge number of repetitions make the goal of extesionally storing and accessing such data very challenging. In this paper, we propose a new methodology, based on an intensional representation of periodic data. The representation model we propose captures the notion of periodic granularity provided by the temporal database glossary, and is an extension of the TSQL2 temporal relational data model. We define the algebraic operators, and introduce access algorithms to cope with them, proving that they are correct with respect to the traditional extesional approach.We also provide an experimental evaluation of our approach.Full Tex
A Comprehensive Approach to 'Now' in Temporal Relational Databases: Semantics and Representation
Full text linkNow-related temporal data play an important role in many applications. Clifford et al.'s approach is a milestone to model the semantics of `now' in temporal relational databases. Several relational representation models for now-related data have been presented; however, the semantics of such representations has not been explicitly studied. Additionally, the definition of a relational algebra to query now-related data is an open problem. We propose the first integrated approach that provides both a neat semantics for now-related data and a compact 1NF representation (data model and relational algebra) for them. Additionally, our approach also extends current approaches to consider (i) domains where it is not always possible to know when changes in the world are recorded in the database and (ii) now-related data with a bound on their persistency in the future. To do so, we explicitly model the notion of temporal indeterminacy in the future for now-related data. The properties of our approach are also analyzed both from a theoretical (semantic correctness and reducibility of the algebra) and from an experimental point of view. Experiments show that, despite the fact that our approach is a major extension to current temporal relational approaches, no significant overhead is added to deal with `now'.No Full Tex
Querying now-relative data
No full textNow-relative temporal data play an important role in most temporal applications, and their management has been proved to impact in a crucial way the efficiency of temporal databases. Though several temporal relational approaches have been developed to deal with now-relative data, none of them has provided a whole temporal algebra to query them. In this paper we overcome such a limitation, by proposing a general algebra which is parametrically adapted to cope with the relational approaches to now-relative data in the literature, i.e., MIN, MAX, NULL and POINT approaches. Besides being general enough to provide a query language for several approaches in the literature, our algebra has been designed in such a way to satisfy several theoretical and practical desiderata: closure with respect to representation languages, correctness with respect to the "consensus" BCDM semantics, reducibility to the standard non-temporal algebra (which involves interoperability with non-temporal relational databases), implementability and ef f iciency. Indeed, the experimental evaluation we have drawn on our implementation has shown that only a slight overhead is added by our treatment of now-relative data (with respect to an approach in which such data are not present).Griffith Sciences, School of Information and Communication TechnologyFull Tex
Representing and querying now-relative relational medical data
Full text linkTemporal information plays a crucial role in medicine. Patients’ clinical records are intrinsically temporal. Thus, in Medical Informatics there is an increasing need to store, support and query temporal data (particularly in relational databases), in order, for instance, to supplement decision-support systems. In this paper, we show that current approaches to relational data have remarkable limitations in the treatment of “now-relative” data (i.e., data holding true at the current time). This can severely compromise their applicability in general, and specifically in the medical context, where “now-relative” data are essential to assess the current status of the patients. We propose a theoretically grounded and application-independent relational approach to cope with now-relative data (which can be paired, e.g., with different decision support systems) overcoming such limitations. We propose a new temporal relational representation, which is the first relational model coping with the temporal indeterminacy intrinsic in now-relative data. We also propose new temporal algebraic operators to query them, supporting the distinction between possible and necessary time, and Allen’s temporal relations between data. We exemplify the impact of our approach, and study the theoretical and computational properties of the new representation and algebra.No Full Tex
A General Approach to Represent and Query Now-Relative Medical Data in Relational Databases
Full text linkNow-related temporal data play an important role in the medical context. Current relational temporal database (TDB) approaches are limited since (i) they (implicitly) assume that the span of time occurring between the time when facts change in the world and the time when the changes are recorded in the database is exactly known, and (ii) do not explicitly provide an extended relational algebra to query now-related data. We propose an approach that, widely adopting AI symbolic manipulation techniques, overcomes the above limitations.No Full Tex
Syntax-Preserving Belief Change Operators for Logic Programs and Hybrid Knowledge Bases
No full textThe recent years have seen several proposals aimed at placing the revision of logic programs within the belief change frameworks established for classical logic. A crucial challenge of this task lies in the nonmonotonicity of standard logic programming semantics. Existing approaches have thus used the
monotonic characterisation via strong equivalence models to develop semantic revision operators, which however neglect any syntactic information. In this thesis, we bridge the gap between semantic and syntactic techniques by adapting three dierent types of constructions from classic belief change. Not
only do they allow us to dene new model-based revision operators that preserve the structure of the programs involved, but they also facilitate a natural denition of contraction operators for logic programs. In particular, we introduce partial meet revision and contraction operators, ensconcement revision and contraction operators, and entrenchment revision and contraction operators for logic programs. We present a new translation of the AGM and belief base revision and contraction postulates to logic programs that is closer to the original formulation than existing translations.Thesis (PhD Doctorate)Doctor of Philosophy (PhD)School of Information and Communication TechnologyScience, Environment, Engineering and TechnologyFull Tex
Intelligent Web Exploration
No full textThe hyperlinked part of the internet known as "the Web" arose without much planning for a future of millions of publishers and countless pieces of online content. It has no in-built mechanism to find anything, so tools external to it were introduced: initially web directories and then search engines. Search engines are based on machine learning and have been extremely successful. However, they have some inherent limitations and cannot, by design, address some needs: they serve the "information locating" need only and not "information discovery". Search engine users have learned to accept them and in many cases do not realise how their search has been limited by shortcomings of the model. Before the advent of the search engine, web directories were the only information-finding tool on the web. They were manually built and could not compete economically with the effciency of search engines. This lead to their virtual extinction, with the effect that the "information discovery" need of users is no longer served by any major information provider. Furthermore, none of the dominant information-finding models account for the person of the user in any meaningful way controllable by (or even visible to) the user. This work proposes a method to combine a search engine, a web directory and a personal information management agent into an intelligent Web Exploration Engine in a way which bridges the gaps between these seemingly unrelated tools. Our hybrid, for which we have developed a proof-of-concept prototype [Kalinov et al., 2010b], allows users to both locate specific data and to discover new information. Information discovery is served by a web directory which is built with the assistance of a dynamic hierarchical classifier we developed [Kalinov et al., 2010a]. The category structure achieved by it is also the basis of a large number of nested search engines, allowing information locating both in general (similar to a "standard" search engine) and in a variety of contexts selectable by the user.Thesis (PhD Doctorate)Doctor of Philosophy (PhD)School of Information and Communication TechnologyScience, Environment, Engineering and TechnologyFull Tex
Extraction and modelling of complex power line corridor
No full textElectricity is crucial in contemporary society, with high-voltage power lines serving as vital components in the power transmission system, facilitating efficient electricity delivery over long distances. The complexity of power line environments, including features like lakes, mountains, and forests, poses challenges for inspection. Meeting the demands for a secure and reliable energy supply requires utility companies to conduct accurate and timely inspections of existing infrastructure and plan for future deployments. Power Line Corridor (PLC) monitoring encompasses electrical components (wires and pylons) and
surrounding objects (vegetation).
In recent years, Light Detection and Ranging (LiDAR) technology has been favoured for PLC inspection due to its active and weather-independent nature of laser scanning. However, today's corridor mapping practice using LiDAR in industries still remains an expensive manual process that is not suitable for a large-scale and rapid commercial compilation of corridor maps. Additionally, most of the research concerning power line extraction and reconstruction from LiDAR data has focused on single power line spans, or regarded bundle conductors as single conductors, while bundle conductor reconstruction is still a very challenging task. Thus, the objective of this thesis is to build an inspection workflow for PLC based on four major steps: (i) extraction of individual objects for a detailed PLC mapping from large-scale LiDAR data; (ii) extraction of sub-conductors; (iii) robust and precise reconstruction of each sub-conductor, and (iv) vegetation monitoring from airborne LiDAR data. [...]Thesis (PhD Doctorate)Doctor of Philosophy (PhD)School of Info & Comm TechScience, Environment, Engineering and TechnologyFull Tex
Towards Stream-Relation Join Processing in Data Streaming Engines
No full textWe are living in a time where a massive 2.5 quintillion bytes of data is generated every day. To realise the value of this data, stream data processing offers a new pro- cessing paradigm that aggregates and analyses large volumes of data quickly. While several commercial Stream Processing Engines (SPEs) are available, it remains dif- ficult to develop stream-based applications. Over the last decade our research has identified and addressed two dominant reasons for this difficulty: Heterogeneity and Stored-Streaming Divide. Heterogeneity highlights the lack of standards in SPEs as well as the wide and changing variety of application requirements. Stored- Streaming Divide is the focus of this thesis. Stored-Streaming Divide emerges from the fact that commercial SPEs treat streaming data and relational data as separate entities even though applications increasingly demand integrated access to both. This integration manifests itself as the join between the stream of fast coming data and relational data sources and is what we call the Stream-Relation Join (SRJ) problem. Two solutions are provided to address the SRJ problem in this thesis. Some commercial SPEs and research projects take a radical approach to ad- dressing the SRJ problem by building an SPE on top of a database from scratch, we call this the stream-relational approach. This approach is cumbersome, it re- quires extensive alteration to the database kernel to process the streaming queries. Alternatively, our approach provides a lean layer that sits between the application and a commercial SPE, which we call the federation layer. This layer extends the database only to the point that it provides just enough functionality to interact with the application and the SPE. In doing so, the federation approach not only efficiently addresses the SRJ problem but ensures the application is portable across a range of commercial SPEs. How to build a federation layer is detailed in this thesisThesis (PhD Doctorate)Doctor of Philosophy (PhD)School of Information and Communication TechnologyScience, Environment, Engineering and TechnologyFull Tex
Tracking Social Media to Understand Visitors' Travel Patterns: a Big Data Approach
No full textTourism involves the movement of people. Visitors move between destinations differently, which creates patterns and networks that link destinations within travel itineraries. These patterns contain valuable information that can be analysed for tourism product development, to improve marketing services and assist in infrastructure and transportation development decisions.
Traditionally, research on visitors' travel patterns relied on data collected from interviews, surveys, or direct observation. Normally the size of this type of data is relatively small, and processing of data collection is costly as well as time consuming. The introduction of big data analytics within tourism research has seen a diversification within data sources, and social media has become a particularly valuable source. Social media generates vast volumes of data, which in addition to text, images, and video content contains additional information in the form of metadata, such as geo-location (where posts were made) and user origins. When combined with advances in big data analytics, this type of data opens an opportunity to model temporal and spatial travel patterns and especially understand the travel pattern drivers behind the travel patterns. Due to the flexibility of social media data collection, understanding visitors' travel patterns is not limited to one destination but can be expanded to the national or global scale.
This thesis took advantage of the value of social media data and big data analytics, in five independent but interconnected chapters to report, and advance knowledge on visitors travel patterns. The thesis is partly comprised of papers with a compilation of five manuscripts in addition to an introduction, an overview of the methodology, and a conclusion chapter.
Manuscript one consisted of a literature review that synthesises the relevant literature relating to social media, tourism, and travel patterns. In the review, key parameters were defined to describe and synthesise the emerging field of tracking visitor flows with social media data. The review discussed commonly used methods and technologies and makes recommendations for future research approaches.
Manuscript two focused on data analytics and theory validation. It applied social network analysis and core-periphery theory using social media data to build the travel networks. The case study followed the travel of Chinese visitors between selected Australian destinations to examine travel structures. The Chinese social media platform Weibo was the data source. The results demonstrated that Chinese visitors in Australia mostly visit iconic Australian destinations first, before visiting destinations classified as semi-core or periphery. The findings indicated the suitability of applying core-periphery theory to social media data. It was also revealed that consideration should be given to the number of destinations within visitors' travel itineraries when analysing the core-periphery travel structures. The rationale for this focus was that the same destination may play a different role if the number of destinations is different.
Manuscript three explored global tourist mobility from social media data and expanded the focus study to a global perspective. The case study investigated travel patterns of Chinese visitors before and after travel to Australia. This work addressed global tourist mobility and how Australia is positioned as an international travel destination. Results showed that Chinese visitors visited thirteen regions before or after their travel to Australia, and they appeared to travel directly from Australia to China, which indicated that Australia was the most prominent gateway country. The proposed method for identifying global travel patterns may be helpful to understand the impacts of the coronavirus pandemic and highlights that tracking mobility is important to understand the spread of diseases as well as opportunities for tourism recovery. Manuscript four proposed a method that identified Chinese travel sentiment in Chinese language. The method was tested using data from Weibo, and a case that focused on Weibo users’ posts related to visiting the Great Barrier Reef (GBR) was conducted. The manuscript detailed the process of capturing the Weibo posts describing the creation of lexicon and presented an algorithm for sentiment calculation. By investigating the sentiment towards different GBR destinations, it was demonstrated that the proposed method was effective in obtaining insights and may be suitable to monitor visitor opinions.
Manuscript five introduced a new methodology that conducted importance-performance analysis (IPA) to understand visitor satisfaction with travel patterns. The new method indirectly measured performance and importance values from publicly available social media data (Weibo). A lexicon-based method for sentiment analysis was applied to identify performance value. Importance was calculated as the adjusted association rule mining algorithm Support, which assessed the frequency of posts at each destination. The results indicated that the proposed methodology offers an opportunity to conduct IPA indirectly from social media data with larger sample sizes, but at a lower cost with flexible data collection. The research used Chinese visitors to Australia as a case study, but the proposed approach could benefit a range of decision-makers directly or indirectly related to tourism.Thesis (PhD Doctorate)Doctor of Philosophy (PhD)Dept Tourism, Sport & Hot MgmtGriffith Business SchoolFull Tex
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