380,470 research outputs found
On restructuring nested relations in partitioned normal form
Relations in partitioned normal form are an important subclass of nested relations. This paper is concerned with the problem of restructuring relations in partitioned normal form to new and potentially very different schemes. The main problem with restructuring is to minimize the amount of information lost during the transformation. A new restructuring operator is defined which minimizes that loss of information. Its definition is refined step by step into more and more computationally efficient versions.Anglai
Editorial of the 2019 Workshop on Very Large Internet of Things (VLIoT)
We are proud of presenting the outcome of this third edition of the "Very Large Internet of Things" (VLIoT) workshop, which was held in Los Angeles (USA) in August 2019, in conjunction with the 45th International Conference on Very Large Data Bases (VLDB). Following the success path of the two previous workshop editions - in Munich (2017) and in Rio de Janeiro (2018) - VLIoT 2019 kept its tradition to be a vivid and high-quality technical forum for researchers and practitioners working with Internet of Things to share their experiences, visions and latest findings, most of them regarding the design, implementation, deployment and management of IoT systems at very large and scale. This editorial of the special issue introduces and introduces all papers presented at the workshop
DisC Diversity: Result Diversification based on Dissimilarity and Coverage
Recently, result diversification has attracted a lot of attention as a means
to improve the quality of results retrieved by user queries. In this paper, we
propose a new, intuitive definition of diversity called DisC diversity. A DisC
diverse subset of a query result contains objects such that each object in the
result is represented by a similar object in the diverse subset and the objects
in the diverse subset are dissimilar to each other. We show that locating a
minimum DisC diverse subset is an NP-hard problem and provide heuristics for
its approximation. We also propose adapting DisC diverse subsets to a different
degree of diversification. We call this operation zooming. We present efficient
implementations of our algorithms based on the M-tree, a spatial index
structure, and experimentally evaluate their performance.Comment: To appear at the 39th International Conference on Very Large Data
Bases (VLDB), August 26-31, 2013, Riva del Garda, Trento, Ital
Indexing multi-dimensional uncertain data with arbitrary probability density functions
Research Session 26: Spatial and Temporal DatabasesIn an "uncertain database", an object o is associated with a multi-dimensional probability density function (pdf), which describes the likelihood that o appears at each position in the data space. A fundamental operation is the "probabilistic range search" which, given a value p q and a rectangular area r q, retrieves the objects that appear in r q with probabilities at least p q. In this paper, we propose the U-tree, an access method designed to optimize both the I/O and CPU time of range retrieval on multi-dimensional imprecise data. The new structure is fully dynamic (i.e., objects can be incrementally inserted/deleted in any order), and does not place any constraints on the data pdfs. We verify the query and update efficiency of U-trees with extensive experiments.postprintThe 31st International Conference on Very Large Data Bases (VLDB 2005), Trondheim, Norway, 30 August-2 September 2005. In Proceedings of 31st VLDB, 2005, v. 3, p. 922-93
Indexing multi-dimensional uncertain data with arbitrary probability density functions
Research Session 26: Spatial and Temporal DatabasesIn an "uncertain database", an object o is associated with a multi-dimensional probability density function (pdf), which describes the likelihood that o appears at each position in the data space. A fundamental operation is the "probabilistic range search" which, given a value p q and a rectangular area r q, retrieves the objects that appear in r q with probabilities at least p q. In this paper, we propose the U-tree, an access method designed to optimize both the I/O and CPU time of range retrieval on multi-dimensional imprecise data. The new structure is fully dynamic (i.e., objects can be incrementally inserted/deleted in any order), and does not place any constraints on the data pdfs. We verify the query and update efficiency of U-trees with extensive experiments.postprintThe 31st International Conference on Very Large Data Bases (VLDB 2005), Trondheim, Norway, 30 August-2 September 2005. In Proceedings of 31st VLDB, 2005, v. 3, p. 922-93
Online Schema Evolution is (Almost) Free for Snapshot Databases
Modern database applications often change their schemas to keep up with the
changing requirements. However, support for online and transactional schema
evolution remains challenging in existing database systems. Specifically, prior
work often takes ad hoc approaches to schema evolution with 'patches' applied
to existing systems, leading to many corner cases and often incomplete
functionality. Applications therefore often have to carefully schedule
downtimes for schema changes, sacrificing availability.
This paper presents Tesseract, a new approach to online and transactional
schema evolution without the aforementioned drawbacks. We design Tesseract
based on a key observation: in widely used multi-versioned database systems,
schema evolution can be modeled as data modification operations that change the
entire table, i.e., data-definition-as-modification (DDaM). This allows us to
support schema almost 'for free' by leveraging the concurrency control
protocol. By simple tweaks to existing snapshot isolation protocols, on a
40-core server we show that under a variety of workloads, Tesseract is able to
provide online, transactional schema evolution without service downtime, and
retain high application performance when schema evolution is in progress.Comment: To appear at Proceedings of the 2023 International Conference on Very
Large Data Bases (VLDB 2023
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