A conceptual trajectory multidimensional model: an application to public transportation

Currently, thanks to global positioning systems technologies and mobile devices equipped with sensors, a lot of data about moving objects can be collected, e.g., data related with the trajectories which are followed by these devices. On the other hand, Data Warehouses (DWs), usually modeled by using a multidimensional view of data, are specialized databases used to support decision-making processes. Unfortunately, conventional DWs offer little support for managing trajectories. Although there are some proposals that deal with trajectory DWs, none of them are devoted to conceptual multidimensional modeling. In this paper, we extend a conceptual spatial multidimensional model by incorporating a trajectory as a fi rst-class concept. In order to show the expediency of our proposal, we illustrate it with an example related to public transportation

Spatio-textual indexing for geographical search on the web

Many web documents refer to specific geographic localities and many people include geographic context in queries to web search engines. Standard web search engines treat the geographical terms in the same way as other terms. This can result in failure to find relevant documents that refer to the place of interest using alternative related names, such as those of included or nearby places. This can be overcome by associating text indexing with spatial indexing methods that exploit geo-tagging procedures to categorise documents with respect to geographic space. We describe three methods for spatio-textual indexing based on multiple spatially indexed text indexes, attaching spatial indexes to the document occurrences of a text index, and merging text index access results with results of access to a spatial index of documents. These schemes are compared experimentally with a conventional text index search engine, using a collection of geo-tagged web documents, and are shown to be able to compete in speed and storage performance with pure text indexing

GET_MOVE: An Efficient and Unifying Spatio-Temporal Pattern Mining Algorithm for Moving Objects

International audienceRecent improvements in positioning technology has led to a much wider availability of massive moving object data. A crucial task is to find the moving objects that travel together. Usually, they are called spatio-temporal pat- terns. Due to the emergence of many different kinds of spatio-temporal patterns in recent years, different approaches have been proposed to extract them. However, each approach only focuses on mining a specific kind of pattern. In addition to the fact that it is a painstaking task due to the large number of algorithms used to mine and manage patterns, it is also time consuming. Additionally, we have to execute these algorithms again whenever new data are added to the existing database. To address these issues, we first redefine spatio-temporal patterns in the itemset context. Secondly, we propose a unifying approach, named GeT Move, using a frequent closed itemset-based spatio-temporal pattern-mining algorithm to mine and manage different spatio-temporal patterns. GeT Move is implemented in two versions which are GeT Move and Incremental GeT Move. Experiments are per- formed on real and synthetic datasets and the experimental results show that our approaches are very effective and outperform existing algorithms in terms of efficiency

Textually Relevant Spatial Skylines

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A Clustering Algorithm to Organize Satellite Hotspot Data for the Purpose of Tracking Bushfires Remotely

This paper proposes a spatiotemporal clustering algorithm and its implementation in the R package spotoroo. This work is motivated by the catastrophic bushfires in Australia throughout the summer of 2019-2020 and made possible by the availability of satellite hotspot data. The algorithm is inspired by two existing spatiotemporal clustering algorithms but makes enhancements to cluster points spatially in conjunction with their movement across consecutive time periods. It also allows for the adjustment of key parameters, if required, for different locations and satellite data sources. Bushfire data from Victoria, Australia, is used to illustrate the algorithm and its use within the package

Mining Frequent Patterns in Uncertain and Relational Data Streams using the Landmark Windows

Todays, in many modern applications, we search for frequent and repeating patterns in the analyzed data sets. In this search, we look for patterns that frequently appear in data set and mark them as frequent patterns to enable users to make decisions based on these discoveries. Most algorithms presented in the context of data stream mining and frequent pattern detection, work either on uncertain data, or use the sliding window model to assess data streams. Sliding window model uses a fixed-size window to only maintain the most recently inserted data and ignores all previous data (or those that are out of its window). Many real-world applications however require maintaining all inserted or obtained data. Therefore, the question arises that whether other window models can be used to find frequent patterns in dynamic streams of uncertain data.In this paper, we used landmark window model and time-fading model to answer that question. The method presented in the form of proposed algorithm, which uses the idea of landmark window model to find frequent patterns in the relational and uncertain data streams, shows a better performance in finding functional dependencies than other methods in this field. Another advantage of this method compared with other methods is that it shows tuples that do not follow a single dependency. This feature can be used to detect inconsistent data in a data set

Spatial Network k-Nearest Neighbor: A Survey and Future Directives

Nearest neighbor algorithms play many roles in our daily lives. From facial recognition to networking applications, many of these are constantly improved for faster processing time and reliable memory management. There are many types of nearest neighbor algorithms. One of them is called k-nearest neighbor (k-NN), a technique that helps to find number of k closest objects from a user location within a specified range of area. k-NN road network algorithm studies have been through various query performance discussions. Each algorithm is usually judged based on query time over few selected parameters which are; number of k, network density and network size. Many studies have claimed different opinions over their techniques and with many results to prove better query performance than others. However, among these techniques, which k-NN road network algorithm has the highest rate of query performance based on the selected parameters? In this paper, reviews on several k nearest neighbor algorithms were made through series of journal extractions and experimentation in order to identify the algorithm that achieves highest query performance. It was found that with the experimentation method, we can identify not only the algorithm’s performance, but also its design flaws and possible future improvement. All methods were tested with some parameters such as varying number of k, road network density and network size. With the results collected, Incremental Expansion Restriction – Pruned Highway Labeling method (IER-PHL) proves to have the best query performance than other methods for most cases