arXiv subjects

Diego Seco

Publications and source records attributed to Diego Seco.

11 recordsLinked to original sources

arXiv2023

When Edge Computing Meets Compact Data Structures

Edge computing enables data processing and storage closer to where the data are created. Given the largely distributed compute environment and the significantly dispersed data distribution, there are increasing demands of data sharing and collaborative processing on the edge. Since data shuffling can dominate the overall execution time of collaborative processing jobs, considering the limited power supply and bandwidth resource in edge environments, it is crucial and valuable to reduce the communication overhead across edge devices. Compared with data compression, compact data structures (CDS) seem to be more suitable in this case, for the capability of allowing data to be queried, navigated, and manipulated directly in a compact form. However, the relevant work about applying CDS to edge computing generally focuses on the intuitive benefit from reduced data size, while few discussions about the challenges are given, not to mention empirical investigations into real-world edge use cases. This research highlights the challenges, opportunities, and potential scenarios of CDS implementation in edge computing. Driven by the use case of shuffling-intensive data analytics, we proposed a three-layer architecture for CDS-aided data processing and particularly studied the feasibility and efficiency of the CDS layer. We expect this research to foster conjoint research efforts on CDS-aided edge data analytics and to make wider practical impacts.

cs.DC↗

arXiv2021

Navigating Planar Topologies in Near-Optimal Space and Time

We show that any embedding of a planar graph can be encoded succinctly while efficiently answering a number of topological queries near-optimally. More precisely, we build on a succinct representation that encodes an embedding of $m$ edges within $4m$ bits, which is close to the information-theoretic lower bound of about $3.58m$. With $4m+o(m)$ bits of space, we show how to answer a number of topological queries relating nodes, edges, and faces, most of them in any time in $ω(1)$. Further, we show that with $O(m)$ bits of space we can solve all those operations in $O(1)$ time.

cs.DS↗

arXiv2021

Faster Compressed Quadtrees

Real-world point sets tend to be clustered, so using a machine word for each point is wasteful. In this paper we first show how a compact representation of quadtrees using $\Oh{1}$ bits per node can break this bound on clustered point sets, while offering efficient range searches. We then describe a new compact quadtree representation based on heavy path decompositions, which supports queries faster than previous compact structures. We present experimental evidence showing that our structure is competitive in practice.

cs.DS↗

arXiv2020

Cell cycle and protein complex dynamics in discovering signaling pathways

Signaling pathways are responsible for the regulation of cell processes, such as monitoring the external environment, transmitting information across membranes, and making cell fate decisions. Given the increasing amount of biological data available and the recent discoveries showing that many diseases are related to the disruption of cellular signal transduction cascades, in silico discovery of signaling pathways in cell biology has become an active research topic in past years. However, reconstruction of signaling pathways remains a challenge mainly because of the need for systematic approaches for predicting causal relationships, like edge direction and activation/inhibition among interacting proteins in the signal flow. We propose an approach for predicting signaling pathways that integrates protein interactions, gene expression, phenotypes, and protein complex information. Our method first finds candidate pathways using a directed-edge-based algorithm and then defines a graph model to include causal activation relationships among proteins, in candidate pathways using cell cycle gene expression and phenotypes to infer consistent pathways in yeast. Then, we incorporate protein complex coverage information for deciding on the final predicted signaling pathways. We show that our approach improves the predictive results of the state of the art using different ranking metrics.

q-bio.MN↗

arXiv2019

On the Reproducibility of Experiments of Indexing Repetitive Document Collections

This work introduces a companion reproducible paper with the aim of allowing the exact replication of the methods, experiments, and results discussed in a previous work [5]. In that parent paper, we proposed many and varied techniques for compressing indexes which exploit that highly repetitive collections are formed mostly of documents that are near-copies of others. More concretely, we describe a replication framework, called uiHRDC (universal indexes for Highly Repetitive Document Collections), that allows our original experimental setup to be easily replicated using various document collections. The corresponding experimentation is carefully explained, providing precise details about the parameters that can be tuned for each indexing solution. Finally, note that we also provide uiHRDC as reproducibility package.

cs.DS↗

arXiv2019

IDEAIS: Smart Voice Assistants to Improve Interaction with SDIs

A critical goal, is that organizations and citizens can easily access the geographic information required for good governance. However, despite the costly efforts of governments to create and implement Spatial Data Infrastructures (SDIs), this goal is far from being achieved. This is partly due to the lack of usability of the geoportals through which the geographic information is accessed. In this position paper, we present IDEAIS, a research network composed of multiple Ibero-American partners to address this usability issue through the use of Intelligent Systems, in particular Smart Voice Assistants, to efficiently recover and access geographic information.

cs.HC↗

arXiv2019

A Compact Representation of Raster Time Series

The raster model is widely used in Geographic Information Systems to represent data that vary continuously in space, such as temperatures, precipitations, elevation, among other spatial attributes. In applications like weather forecast systems, not just a single raster, but a sequence of rasters covering the same region at different timestamps, known as a raster time series, needs to be stored and queried. Compact data structures have proven successful to provide space-efficient representations of rasters with query capabilities. Hence, a naive approach to save space is to use such a representation for each raster in a time series. However, in this paper we show that it is possible to take advantage of the temporal locality that exists in a raster time series to reduce the space necessary to store it while keeping competitive query times for several types of queries.

cs.DS↗

arXiv2019

Faster and Smaller Two-Level Index for Network-based Trajectories

Two-level indexes have been widely used to handle trajectories of moving objects that are constrained to a network. The top-level of these indexes handles the spatial dimension, whereas the bottom level handles the temporal dimension. The latter turns out to be an instance of the interval-intersection problem, but it has been tackled by non-specialized spatial indexes. In this work, we propose the use of a compact data structure on the bottom level of these indexes. Our experimental evaluation shows that our approach is both faster and smaller than existing solutions.

cs.DS↗

arXiv2018

Compact Representations of Event Sequences

We introduce a new technique for the efficient management of large sequences of multidimensional 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.

cs.DS↗

arXiv2016

Parallel Construction of Wavelet Trees on Multicore Architectures

The wavelet tree has become a very useful data structure to efficiently represent and query large volumes of data in many different domains, from bioinformatics to geographic information systems. One problem with wavelet trees is their construction time. In this paper, we introduce two algorithms that reduce the time complexity of a wavelet tree's construction by taking advantage of nowadays ubiquitous multicore machines. Our first algorithm constructs all the levels of the wavelet in parallel in $O(n)$ time and $O(n\lgσ+ σ\lg n)$ bits of working space, where $n$ is the size of the input sequence and $σ$ is the size of the alphabet. Our second algorithm constructs the wavelet tree in a domain-decomposition fashion, using our first algorithm in each segment, reaching $O(\lg n)$ time and $O(n\lgσ+ pσ\lg n/\lgσ)$ bits of extra space, where $p$ is the number of available cores. Both algorithms are practical and report good speedup for large real datasets.

cs.DS↗

arXiv2016

Aggregated 2D Range Queries on Clustered Points

Efficient processing of aggregated range queries on two-dimensional grids is a common requirement in information retrieval and data mining systems, for example in Geographic Information Systems and OLAP cubes. We introduce a technique to represent grids supporting aggregated range queries that requires little space when the data points in the grid are clustered, which is common in practice. We show how this general technique can be used to support two important types of aggregated queries, which are ranked range queries and counting range queries. Our experimental evaluation shows that this technique can speed up aggregated queries up to more than an order of magnitude, with a small space overhead.

cs.DS↗