In this work, we explore different solutions for the problem of representing and querying geographical objects at different scales (zoom levels). In Geographic Information Systems (GIS), the maps we see when looking at the world at a planetary scale versus seeing the detailed coast of one province of a particular country are typically different, since GIS stores several maps, each with the appropriate level of detail for visualizing at a different scale. With the technique presented in this paper, it is possible to store and query the data of multiple geographical objects at different levels of scale, drastically reducing space consumption.

In the last two decades, word-based text compression has shown to be the key to efficiently handling large collections of text not only due to yielding important storage savings but, more importantly, because it allowed boosting the performance of some traditional text retrieval systems. The reason is that when the appropriate compression techniques are chosen, compressed text search becomes much faster than searches on plain text, and retrieval/decompression could start at any part of the compressed data, hence allowing to keep the text collection compressed all the time. Word-based text compressors have been typically compared in terms of their compression effectiveness, encoding/decoding speed, and performance when searching for words. In this paper, we show that compression also has benefits in terms of energy efficiency when performing word-based searches. Particularly, our experiments considering searches performed over uncompressed text and text compressed with the most well-suited compressors for text databases showed energy savings of around 30–70

The Block Tree (BT) is a novel compact data structure designed to compress sequence collections. It obtains compression ratios close to Lempel-Ziv and supports efficient direct access to any substring. The BT divides the text recursively into fixed-size blocks and those appearing earlier are represented with pointers. On repetitive collections, a few blocks can represent all the others, and thus the BT reduces the size by orders of magnitude. In this paper we extend the BT to two dimensions, to exploit repetitiveness in collections of images, graphs, and maps. This two-dimensional Block Tree divides the image regularly into subimages and replaces some of them by pointers to other occurrences thereof. We develop a specific variant aimed at compressing the adjacency matrices of Web graphs, obtaining space reductions of up to 50% compared with the k2-tree, which is the best alternative supporting direct and reverse navigation in the graph.

GraCT and ContaCT were the first compressed data structures to represent object trajectories, demonstrating that it was possible to use orders of magnitude less space than classical indexes while staying competitive in query times. In this paper we considerably enhance their space, query capabilities, and time performance with three contributions. (1) We design and evaluate algorithms for more sophisticated nearest neighbor queries, finding the trajectories closest to a given trajectory or to a given point during a time interval. (2) We modify the data structure used to sample the spatial positions of the objects along time. This improves the performance on the classic spatio-temporal and the nearest neighbor queries, by orders of magnitude in some cases. (3) We introduce RelaCT, a tradeoff between the faster and larger ContaCT and the smaller and slower GraCT, offering a new relevant space-time tradeoff for large repetitive datasets of trajectories.

Distance-based Join Queries (DJQs) have multiple applications in spatial databases, Geographic Information Systems, and other areas. The K Closest Pairs Query (KCPQ) and the ε Distance Join Query (εDJQ) are well-known DJQs that have been widely studied and can be solved using plane-sweep techniques, which are efficient but must keep the whole datasets in main memory. In this work, we propose DJQ algorithms that work with data represented using a k2-tree, a compact data structure for binary grids. Our algorithms solve KCPQ and εDJQ queries, as well as several window-constrained variants, taking advantage of the indexing capabilities of k2-trees to efficiently answer queries without the need to decompress the data. Our experimental evaluation with large datasets shows that k2-tree algorithms are up to 5 times faster than plane-sweep algorithms in KCPQ, and 5–30 times faster in εDJQ. In variants that are window-constrained, our algorithms are competitive in most scenarios and faster for large windows. Additionally, our algorithms are not very affected by the distribution of the data and yield much more predictable query times, showing up to 30 times smaller variance in query times than plane sweep, depending on the location of the query window.

Purpose This paper aims to build an indoor positioning system capable of tracking residents and caregivers and feeding a novel information system with this data. This information system introduces three main interesting modules: a compact data structure to efficiently store the gathered data, an activity deduction module that add semantics to the raw captured trajectories and a user-friendly interface able to display this spatio-temporal information. Design/methodology/approach Their proposal was built following an iterative and incremental development. Nursing home managers cooperated in the requirement composition phase, while some residents and caregivers contributed testing the system with their real trajectories. Findings Their contribution is a functional information system that can evaluate the quality of healthcare provided to residents and assess the efficiency of the nursing home as a business. This information system includes a novel state-of-the-art compact data structure to efficiently work with the captured data. Originality/value A new system has been designed and implemented using a wide range of technologies to support the necessities of a real enterprise. The rudimentary position tracking systems were upgraded to a feasible automatic indoor positioning solution. The amount of information generated by this new strategy is dealt with compact data structures, reducing space usage without jeopardizing query times. Those compressed positions are tagged with semantic information that enables individual activity analysis. Last, a user-friendly interface lets the final user interact with the gathered and calculated information. As future work, the authors plan to improve the integration of this new information system with other systems already in use in indoor mobile workforce environments.

In this work, we present the design of a novel geographic information tool for the analysis of public transportation data. The widespread integration of user traveler cards have enabled public transport operators to generate and store large amounts of data related to user movements within the transport network. However, these authorities are seldom equipped to efficiently exploit these data in order to produce a comprehensible analysis of the transport network usage. This is not only due to the sheer amount of data in need of processing, but also because most public transport operators only validate the travel card on boarding, whereas data referring to transfers and alightings are generally unavailable. Thus, the system we propose not only addresses efficient storage and exploitation of big datasets, but also the reconstruction of complete journeys by using a prediction algorithm to deduce the alighting stop for each boarding. Furthermore, we also provide the transport operators with easy-to-use means of visualizing and analyzing the data through a graphical interface.

In this work, we present a compact method for storing and indexing users’ trips across transport networks. This research is part of a larger project focused on providing transportation managers with the tools to analyze the need for improvements in public transportation networks. Specifically, we focus on addressing the problem of grouping the massive amount of data from the records of traveller cards as coherent trips that describe the trajectory of users from one origin stop to a destination using the transport network, and the efficient storage and querying of those trips. We propose two alternative methods capable of achieving a space reduction between 60 to 80% with respect to storing the raw trip data. In addition, our proposed methods are auto-indexed, allowing fast querying of the trip data to answer relevant questions for public transport administrators, such as how many trips have been made from an origin to a destination or how many trips made a transfer in a certain station.

Maps need different representations of the same cartographic object in order to display it at several scale levels. When a user zooms in, the object should depict every detail as it is the main focus on the map. On the contrary, when the user zooms out, there is no need to display every single detail of the object as most would become as small as to be invisible. A common technique to speed up the display process is storing several representations for the same object at different scales. However, this technique raises redundancy, and therefore it increases space usage. This work aims at suppressing that redundancy by introducing four alternatives that use a unique representation for each cartographic object while still being able to retrieve simplified versions of them depending on the requested scale.

Compact data structures can represent data with usually a much smaller memory footprint than its plain representation. In addition to maintaining the data in a form that uses less space, they allow us to efficiently access and query the data in its compact form. The $$k^2$$ -tree is a self-indexed, compact data structure used to represent binary matrices, that can also be used to represent points in a spatial dataset. Efficient processing of the Distance-based Join Queries (DJQs) is of great importance in spatial databases due to its wide area of application. Two of the most representative and known DJQs are the K Closest Pairs Query (KCPQ) and the $$\varepsilon $$ Distance Join Query ( $$\varepsilon $$ DJQ). These types of join queries are executed over two spatial datasets and can be solved by plane-sweep algorithms, which are efficient but with great requirements of RAM, to be able to fit the whole datasets into main memory. In this work, we present new and efficient algorithms to implement DJQs over the $$k^2$$ -tree representation of the spatial datasets, experimentally showing that these algorithms are competitive in query times, with much lower memory requirements.

Our project is aimed at the creation of SIGTRANS, a tool focused on addressing the need of efficiently storing and analyzing the vast amount of data related to the use of public transport networks. This is a highly relevant research topic given the changes urban mobility is experiencing, including but not limited to those motivated by climate change. We will provide transport authorities and operators with a system, combining the use of GIS technologies, compact data structures and advanced algorithms, to facilitate the exploration and exploitation of the available data. This data refers to both the offer in terms of infrastructure and mobility services available for the citizens, and the demand (that is, the use citizens are expecting out of these services). The analysis of this data will then serve as the foundation for further improvements to public transport services.

The resource description framework (RDF) has gained popularity as a format for the standardized publication and exchange of information in the Web of Data. In this paper, we introduce RDFCSA , a compressed representation of RDF datasets that in addition supports efficient querying. RDFCSA regards the triples of the RDF store as short circular strings and applies suffix sorting on those strings, so that triple-pattern queries reduce to prefix searching on the string set. The RDF store is then represented compactly using a compressed suffix array (CSA), a proved technology in text indexing that efficiently supports prefix searches. Our experiments show that RDFCSA is competitive with state-of-the-art alternatives. It compresses the raw data to 60% of its size, close to the most compact alternatives. While most alternatives perform better in some kinds of triple-patterns than in others, RDFCSA features fast and consistent query times, a few microseconds per result in all cases. This enables efficiently supporting join queries by using either merge- or chaining-join strategies over the triple patterns coupled with some specific optimizations such as variable filling. Our experiments on binary joins show that RDFCSA is faster than the alternatives in most cases.

Geographic Information Systems (GIS) have spread all over our technological environment in the last decade. The inclusion of GPS technologies in everyday portable devices along with the creation of massive shareable geographical data banks has boosted the rise of geoinformatics. Despite the technological maturity of this field, there are still relevant research challenges concerning efficient information storage and representation. One of the most powerful techniques to tackle these issues is designing new Succinct Data Structures (SDS). These structures are defined by three main characteristics: they use a compact representation of the data, they have self-index properties and, as a consequence, they do not need decompression to process the enclosed information. Thus, SDS are not only capable of storing geographical data using as little space as possible, but they can also solve queries efficiently without any previous decompression. This work introduces how SDS can be successfully applied in the GIS context through several novel approaches and practical use cases.

We present a novel representation of enriched trajectories of a mobile workforce management system. In this system, employees are tracked during their working day and both their routes and the tasks performed at each time instant are recorded. Our proposal tackles the representation of this information paying special attention to the space footprint without neglecting query time. We performed experiments using real and synthetic datasets where we show the compression effectiveness as well as the efficiency at query time. Our results showed that our proposal yields promising results in terms of the space needed to represent both users' locations and activities while performing access queries to the original data within microseconds.

As the number of vehicles and devices equipped with GPS technology has grown explosively, an urgent need has arisen for time- and space-efficient data structures to represent their trajectories. The most commonly desired queries are the following: queries about an object’s trajectory, range queries, and nearest neighbor queries. In this paper, we consider that the objects can move freely and we present a new compressed data structure for storing their trajectories, based on a combination of logs and snapshots, with the logs storing sequences of the objects’ relative movements and the snapshots storing their absolute positions sampled at regular time intervals. We call our data structure ContaCT because it provides Constant- time access to Compressed Trajectories. Its logs are based on a compact partial-sums data structure that returns cumulative displacement in constant time, and allows us to compute in constant time any object’s position at any instant, enabling a speedup when processing several other queries. We have compared ContaCT experimentally with another compact data structure for trajectories, called GraCT, and with a classic spatio-temporal index, the MVR-tree. Our results show that ContaCT outperforms the MVR-tree by orders of magnitude in space and also outperforms the compressed representation in time performance.

The new opportunities generated by the data-driven economy in the manufacturing industry have caused many companies opt for it. However, the size of time series data that need to be captured creates the problem of having to assume high storage costs. Moreover, these costs, which are constantly growing, begin to have an impact on the profitability of companies. Thus, in this scenario, the need arises to develop techniques that allow obtaining reduced representations of the time series. In this paper, we present a lossless compression method for industrial time series that allows an efficient access. That is, our aim goes beyond pure compression, where the usual way to access the data requires a complete decompression of the dataset before processing it. Instead, our method allows decompressing portions of the dataset, and moreover, it allows direct querying the compressed data. Thus, the proposed method combines the efficient access, typical of lossy methods, with the lossless compression. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

The raster model is commonly used for the representation of images in many domains, and is especially useful in Geographic Information Systems (GIS) to store information about continuous variables of the space (elevation, temperature, etc.). Current representations of raster data are usually designed for external memory or, when stored in main memory, lack efficient query capabilities. In this paper we propose compact representations to efficiently store and query raster datasets in main memory. We present different representations for binary raster data, general raster data and time-evolving raster data. We experimentally compare our proposals with traditional storage mechanisms such as linear quadtrees or compressed GeoTIFF files. Results show that our structures are up to 10 times smaller than classical linear quadtrees, and even comparable in space to non-querieable representations of raster data, while efficiently answering a number of typical queries.

We present a compact data structure to represent both the duration and length of homogeneous segments of trajectories from moving objects in a way that, as a data warehouse, it allows us to efficiently answer cumulative queries. The division of trajectories into relevant segments has been studied in the literature under the topic of Trajectory Segmentation. In this paper, we design a data structure to compactly represent them and the algorithms to answer the more relevant queries. We experimentally evaluate our proposal in the real context of an enterprise with mobile workers (truck drivers) where we aim at analyzing the time they spend in different activities. To test our proposal under higher stress conditions we generated a huge amount of synthetic realistic trajectories and evaluated our system with those data to have a good idea about its space needs and its efficiency when answering different types of queries.

We present a new compact representation to efficiently store and query large RDF datasets in main memory. Our proposal, called BMatrix, is based on the k-tree, a data structure devised to represent binary matrices in a compressed way, and aims at improving the results of previous state-of-the-art alternatives, especially in datasets with a relatively large number of predicates. We introduce our technique, together with some improvements on the basic k-tree that can be applied to our solution in order to boost compression. Experimental results in the flagship RDF dataset DBPedia show that our proposal achieves better compression than existing alternatives, while yielding competitive query times, particularly in the most frequent triple patterns and in queries with unbound predicate, in which we outperform existing solutions.

We present a new compact representation to efficiently store and query large RDF datasets in main memory. Our proposal, called BMatrix, is based on the k2-tree, a data structure devised to represent binary matrices in a compressed way, and aims at improving the results of previous state-of-the-art alternatives, especially in datasets with a relatively large number of predicates. We introduce our technique, together with some improvements on the basic k2-tree that can be applied to our solution in order to boost compression. Experimental results in the flagship RDF dataset DBPedia show that our proposal achieves better compression than existing alternatives, while yielding competitive query times, particularly in the most frequent triple patterns and in queries with unbound predicate, in which we outperform existing solutions.