arXiv subjects

Dimitris Papadias

Publications and source records attributed to Dimitris Papadias.

4 recordsLinked to original sources

arXiv2026

Disk-Based Interval Indexes Under the Increasing Ending Time Assumption

Indexes for large collections of intervals are common in temporal databases, where each record has a lifespan, or validity interval. Despite their conceptual differences, we demonstrate that interval indexes can be captured by some corner structure in a 2D space. This representation facilitates the optimization of query processing by identifying nodes that must contain query results versus nodes that may contain results. In addition, we explore the assumption that intervals arrive in order of increasing ending time (IET) to develop disk-based indexes that have compact size, efficient insertions, and fast query processing. Specifically, we first develop CEB, an index in the corner space defined by the interval center and endpoint. Our second contribution is TIDE, which organizes intervals by their duration and endpoint. CEB and TIDE adopt a two-layer architecture, where the leaf nodes of a top tree (ordering intervals by their center or duration) correspond to the root nodes of bottom trees, ordering intervals by their endpoints. Both top and bottom trees are append-only B+-trees to facilitate fast insertions. CEB and TIDE outperform state-of-the-art competitors in terms of index size and insertion speed. In addition, TIDE is always faster in query processing, sometimes by orders of magnitude.

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arXiv2024

Fast and Adaptive Bulk Loading of Multidimensional Points

Existing methods for bulk loading disk-based multidimensional points involve multiple applications of external sorting. In this paper, we propose techniques that apply linear scan, and are therefore significantly faster. The resulting FMBI Index possesses several desirable properties, including almost full and square nodes with zero overlap, and has excellent query performance. As a second contribution, we develop an adaptive version AMBI, which utilizes the query workload to build a partial index only for parts of the data space that contain query results. Finally, we extend FMBI and AMBI to parallel bulk loading and query processing in distributed systems. An extensive experimental evaluation with real datasets confirms that FMBI and AMBI clearly outperform competitors in terms of combined index construction and query processing cost, sometimes by orders of magnitude.

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arXiv2017

Uncertain Graph Sparsification

Uncertain graphs are prevalent in several applications including communications systems, biological databases and social networks. The ever increasing size of the underlying data renders both graph storage and query processing extremely expensive. Sparsification has often been used to reduce the size of deterministic graphs by maintaining only the important edges. However, adaptation of deterministic sparsification methods fails in the uncertain setting. To overcome this problem, we introduce the first sparsification techniques aimed explicitly at uncertain graphs. The proposed methods reduce the number of edges and redistribute their probabilities in order to decrease the graph size, while preserving its underlying structure. The resulting graph can be used to efficiently and accurately approximate any query and mining tasks on the original graph. An extensive experimental evaluation with real and synthetic datasets illustrates the effectiveness of our techniques on several common graph tasks, including clustering coefficient, page rank, reliability and shortest path distance.

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arXiv2017

Engineering Methods for Differentially Private Histograms: Efficiency Beyond Utility

Publishing histograms with $ε$-differential privacy has been studied extensively in the literature. Existing schemes aim at maximizing the utility of the published data, while previous experimental evaluations analyze the privacy/utility trade-off. In this paper we provide the first experimental evaluation of differentially private methods that goes beyond utility, emphasizing also on another important aspect, namely efficiency. Towards this end, we first observe that all existing schemes are comprised of a small set of common blocks. We then optimize and choose the best implementation for each block, determine the combinations of blocks that capture the entire literature, and propose novel block combinations. We qualitatively assess the quality of the schemes based on the skyline of efficiency and utility, i.e., based on whether a method is dominated on both aspects or not. Using exhaustive experiments on four real datasets with different characteristics, we conclude that there are always trade-offs in terms of utility and efficiency. We demonstrate that the schemes derived from our novel block combinations provide the best trade-offs for time critical applications. Our work can serve as a guide to help practitioners engineer a differentially private histogram scheme depending on their application requirements.

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