Special issue on spatio-temporal theories and models for environmental, urban and social sciences: where do we stand ?

This extensive editorial of this special journal issue follows a workshop organized in conjunction with the 11th International Conference on Spatial Information Theory (COSIT 2013) in September 2013 in Scarborough, UK. The objective of this international workshop was to bring together representatives from these different disciplinary communities, and integrate academics, students, and practitioners for a one-day workshop on spatiotemporal concepts and theories. This editorial introduces the special issue, the research objectives the workshop followed and some of the main contributions as well as the theoretical achievements and research perspectives left

Reporting flock patterns

Data representing moving objects is rapidly getting more available, especially in the area of wildlife GPS tracking. It is a central belief that information is hidden in large data sets in the form of interesting patterns. One of the most common spatio-temporal patterns sought after is flocks. A flock is a large enough subset of objects moving along paths close to each other for a certain pre-defined time. We give a new definition that we argue is more realistic than the previous ones, and by the use of techniques from computational geometry we present fast algorithms to detect and report flocks. The algorithms are analysed both theoretically and experimentally

Parallel and distributed clustering framework for big spatial data mining

Clustering techniques are very attractive for identifying and extracting patterns of interests from datasets. However, their application to very large spatial datasets presents numerous challenges such as high-dimensionality, heterogeneity, and high complexity of some algorithms. Distributed clustering techniques constitute a very good alternative to the Big Data challenges (e.g., Volume, Variety, Veracity, and Velocity). In this paper, we developed and implemented a Dynamic Parallel and Distributed clustering (DPDC) approach that can analyse Big Data within a reasonable response time and produce accurate results, by using existing and current computing and storage infrastructure, such as cloud computing. The DPDC approach consists of two phases. The first phase is fully parallel and it generates local clusters and the second phase aggregates the local results to obtain global clusters. The aggregation phase is designed in such a way that the final clusters are compact and accurate while the overall process is efficient in time and memory allocation. DPDC was thoroughly tested and compared to well-known clustering algorithms BIRCH and CURE. The results show that the approach not only produces high-quality results but also scales up very well by taking advantage of the Hadoop MapReduce paradigm or any distributed system

RUBIK: Efficient Threshold Queries on Massive Time Series

An increasing number of applications from finance, meteorology, science and others are producing time series as output. The analysis of the vast amount of time series is key to understand the phenomena studied, particularly in the simulation sciences, where the analysis of time series resulting from simulation allows scientists to refine the model simulated. Existing approaches to query time series typically keep a compact representation in main memory, use it to answer queries approximately and then access the exact time series data on disk to validate the result. The more precise the in-memory representation, the fewer disk accesses are needed to validate the result. With the massive sizes of today's datasets, however, current in-memory representations oftentimes no longer fit into main memory. To make them fit, their precision has to be reduced considerably resulting in substantial disk access which impedes query execution today and limits scalability for even bigger datasets in the future. In this paper we develop RUBIK, a novel approach to compressing and indexing time series. RUBIK exploits that time series in many applications and particularly in the simulation sciences are similar to each other. It compresses similar time series, i.e., observation values as well as time information, achieving better space efficiency and improved precision. RUBIK translates threshold queries into two dimensional spatial queries and efficiently executes them on the compressed time series by exploiting the pruning power of a tree structure to find the result, thereby outperforming the state-of-the-art by a factor of between 6 and 23. As our experiments further indicate, exploiting similarity within and between time series is crucial to make query execution scale and to ultimately decouple query execution time from the growth of the data (size and number of time series)

Visualization for exploratory analysis of spatio-temporal data

Analysis of spatio-temporal data has become critical with the emerge of ubiquitous location sensor technologies and applications keeping track of such data. Especially with the widespread availability of low cost GPS devices, it is possible to record data about the location of people and objects at a large scale. Data visualization plays a key role in the successful analysis of these kind of data. Due to the complex nature of this analysis process, current approaches and analytical tools fail to help spatio-temporal thinking and they are not effective when solving large range of problems. In this work, we propose an interactive visualization tool to support human analyst understand user behaviors by analyzing location patterns and anomalies in massive collections of spatio-temporal data. The tool that we developed within this work combines a geovisualization framework with 3D visualizations and histograms. Tool's effectiveness in exploratory analysis is tested by trend analysis and anomaly detection in a real mobile service dataset with almost 1.5 million rows

Technical report of data mining

No abstract availabl

Context-Specific Preference Learning of One Dimensional Quantitative Geospatial Attributes Using a Neuro-Fuzzy Approach

Change detection is a topic of great importance for modern geospatial information systems. Digital aerial imagery provides an excellent medium to capture geospatial information. Rapidly evolving environments, and the availability of increasing amounts of diverse, multiresolutional imagery bring forward the need for frequent updates of these datasets. Analysis and query of spatial data using potentially outdated data may yield results that are sometimes invalid. Due to measurement errors (systematic, random) and incomplete knowledge of information (uncertainty) it is ambiguous if a change in a spatial dataset has really occurred. Therefore we need to develop reliable, fast, and automated procedures that will effectively report, based on information from a new image, if a change has actually occurred or this change is simply the result of uncertainty. This thesis introduces a novel methodology for change detection in spatial objects using aerial digital imagery. The uncertainty of the extraction is used as a quality estimate in order to determine whether change has occurred. For this goal, we develop a fuzzy-logic system to estimate uncertainty values fiom the results of automated object extraction using active contour models (a.k.a. snakes). The differential snakes change detection algorithm is an extension of traditional snakes that incorporates previous information (i.e., shape of object and uncertainty of extraction) as energy functionals. This process is followed by a procedure in which we examine the improvement of the uncertainty at the absence of change (versioning). Also, we introduce a post-extraction method for improving the object extraction accuracy. In addition to linear objects, in this thesis we extend differential snakes to track deformations of areal objects (e.g., lake flooding, oil spills). From the polygonal description of a spatial object we can track its trajectory and areal changes. Differential snakes can also be used as the basis for similarity indices for areal objects. These indices are based on areal moments that are invariant under general affine transformation. Experimental results of the differential snakes change detection algorithm demonstrate their performance. More specifically, we show that the differential snakes minimize the false positives in change detection and track reliably object deformations

Mining climate data for shire level wheat yield predictions in Western Australia

Climate change and the reduction of available agricultural land are two of the most important factors that affect global food production especially in terms of wheat stores. An ever increasing world population places a huge demand on these resources. Consequently, there is a dire need to optimise food production. Estimations of crop yield for the South West agricultural region of Western Australia have usually been based on statistical analyses by the Department of Agriculture and Food in Western Australia. Their estimations involve a system of crop planting recommendations and yield prediction tools based on crop variety trials. However, many crop failures arise from adherence to these crop recommendations by farmers that were contrary to the reported estimations. Consequently, the Department has sought to investigate new avenues for analyses that improve their estimations and recommendations. This thesis explores a new approach in the way analyses are carried out. This is done through the introduction of new methods of analyses such as data mining and online analytical processing in the strategy. Additionally, this research attempts to provide a better understanding of the effects of both gradual variation parameters such as soil type, and continuous variation parameters such as rainfall and temperature, on the wheat yields. The ultimate aim of the research is to enhance the prediction efficiency of wheat yields. The task was formidable due to the complex and dichotomous mixture of gradual and continuous variability data that required successive information transformations. It necessitated the progressive moulding of the data into useful information, practical knowledge and effective industry practices. Ultimately, this new direction is to improve the crop predictions and to thereby reduce crop failures. The research journey involved data exploration, grappling with the complexity of Geographic Information System (GIS), discovering and learning data compatible software tools, and forging an effective processing method through an iterative cycle of action research experimentation. A series of trials was conducted to determine the combined effects of rainfall and temperature variations on wheat crop yields. These experiments specifically related to the South Western Agricultural region of Western Australia. The study focused on wheat producing shires within the study area. The investigations involved a combination of macro and micro analyses techniques for visual data mining and data mining classification techniques, respectively. The research activities revealed that wheat yield was most dependent upon rainfall and temperature. In addition, it showed that rainfall cyclically affected the temperature and soil type due to the moisture retention of crop growing locations. Results from the regression analyses, showed that the statistical prediction of wheat yields from historical data, may be enhanced by data mining techniques including classification. The main contribution to knowledge as a consequence of this research was the provision of an alternate and supplementary method of wheat crop prediction within the study area. Another contribution was the division of the study area into a GIS surface grid of 100 hectare cells upon which the interpolated data was projected. Furthermore, the proposed framework within this thesis offers other researchers, with similarly structured complex data, the benefits of a general processing pathway to enable them to navigate their own investigations through variegated analytical exploration spaces. In addition, it offers insights and suggestions for future directions in other contextual research explorations

An Ontology-Based Approach for Closed-Loop Product Lifecycle Management

The main goal of the Product Lifecycle Management (PLM) is the management of all the data associated to a product during its lifecycle. Lifecycle data is being generated by events and actions (of various lifecycle agents which are humans and/or software systems) and it is distributed along the product's lifecycle phases: Beginning of Life (BOL) including design and manufacturing, Middle of Life (MOL) including usage and maintenance and End of Life (EOL) including recycling, disposal or other options. Closed-Loop PLM extends the meaning of PLM in order to close the loop of the information among the different lifecycle phases. The idea is that information of MOL could be used at the EOL stage to support deciding the most appropriate EOL option (especially to make decision for re-manufacturing and re-use) and combined with the EOL information it could be used as feedback in the BOL for improving the new generations of the product. Several PLM models have been developed utilising various technologies and methods towards providing aspects of the Closed-Loop PLM concept. Ontologies are rapidly becoming popular in various research fields. There is a tendency both in converting existing models into ontology-based models, and in creating new ontology-based models from scratch. The aim of this dissertation is to include the advantages and features provided by the ontologies into PLM models towards achieving Closed-Loop PLM. Hence, an ontology model of a Product Data and Knowledge Management Semantic Object Model for PLM has been developed. The transformation process of the model into an ontology-based one, using Web Ontology Language-Description Logic (OWL-DL), is described in detail. The background and the motives for converting existing PLM models to ontologies are also provided. The new model facilitates several of the OWL-DL capabilities, while maintaining previously achieved characteristics. Furthermore, case studies based on various application scenarios, are presented. These case studies deal with data integration and interoperability problems, in which a significant number of reasoning capabilities is implemented, and highlight the utilisation of the developed model. Moreover, in this work, a generic concept has been developed, tackling the time treatment in PLM models. Time is the only fundamental dimension which exists along the entire life of an artefact and it affects all artefacts and their qualities. Most commonly in PLM models, time is an attribute in parts such as "activities" and "events" or is a separate part of the model ("four dimensional models"). In this work the concept is that time should not be one part of the model, but it should be the basis of the model, and all other elements should be parts of it. Thus, we introduce the "Duration of Time concept". According to this concept all aspects and elements of a model are parts of time. Case studies demonstrate the applicability and the advantages of the concept in comparison to existing methodologies