[Abstract] 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.

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 D-v2v, a new dynamic (one-pass) variable-to-variable compressor. Variable-to-variable compression aims at using a modeler that gathers variable-length input symbols and a variable-length statistical coder that assigns shorter codewords to the more frequent symbols. In D-v2v, we process the input text word-wise to gather variable-length symbols that can be either terminals (new words) or non-terminals, subsequences of words seen before in the input text. Those input symbols are set in a vocabulary that is kept sorted by frequency. Therefore, those symbols can be easily encoded with dense codes. Our D-v2v permits real-time transmission of data, i.e. compression/transmission can begin as soon as data become available. Our experiments show thatD-v2vis able to overcome the compression ratios of the v2vDC, the state-of-the-art semi-static variable-to-variable compressor, and to almost reach p7zip values. It also draws a competitive performance at both compression and decompression.

We introduce a new technique for the efficient management of large sequences of multi-dimensional data, which takes advantage of regularities that arise in real-world datasets and supports different types of aggregation queries. More importantly, our representation is flexible in the sense that the relevant dimensions and queries may be used to guide the construction process, easily providing a space-time tradeoff depending on the relevant queries in the domain. We provide two alternative representations for sequences of multidimensional data and describe the techniques to efficiently store the datasets and to perform aggregation queries over the compressed representation. We perform experimental evaluation on realistic datasets, showing the space efficiency and query capabilities of our proposal.