Entity–attribute–value model

Résumé

An entity–attribute–value model (EAV) is a data model optimized for the space-efficient storage of sparse—or ad-hoc—property or data values, intended for situations where runtime usage patterns are arbitrary, subject to user variation, or otherwise unforseeable using a fixed design. The use-case targets applications which offer a large or rich system of defined property types, which are in turn appropriate to a wide set of entities, but where typically only a small, specific selection of these are instantated (or persisted) for a given entity. Therefore, this type of data model relates to the mathematical notion of a sparse matrix. EAV is also known as object–attribute–value model, vertical database model, and open schema. Data structure This data representation is analogous to space-efficient methods of storing a sparse matrix, where only non-empty values are stored. In an EAV data model, each attribute–value pair is a fact describing an entity, and a row in an EAV table

Source officielle

https://en.wikipedia.org/wiki/Entity%E2%80%93attribute%E2%80%93value_model

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Monitoring distributed fragmented skylines

Odysseas Papapetrou

Distributed skyline computation is important for a wide range of domains, from distributed and web-based systems to ISP-network monitoring and distributed databases. The problem is particularly challenging in dynamic distributed settings, where the goal is to efficiently monitor a continuous skyline query over a collection of distributed streams. All existing work relies on the assumption of a single point of reference for object attributes/dimensions: objects may be vertically or horizontally partitioned, but the accurate value of each dimension for each object is always maintained by a single site. This assumption is unrealistic for several distributed applications, where object information is fragmented over a set of distributed streams (each monitored by a different site) and needs to be aggregated (e.g., averaged) across several sites. Furthermore, it is frequently useful to define skyline dimensions through complex functions over the aggregated objects, which raises further challenges for dealing with distribution and object fragmentation. We present the first known distributed algorithms for continuous monitoring of skylines over complex functions of fragmented multi-dimensional objects. Our algorithms rely on decomposition of the skyline monitoring problem to a select set of distributed threshold-crossing queries, which can be monitored locally at each site. We propose several optimizations, including: (a) a technique for adaptively determining the most efficient monitoring strategy for each object, (b) an approximate monitoring technique, and (c) a strategy that reduces communication overhead by grouping together threshold-crossing queries. Furthermore, we discuss how our proposed algorithms can be used to address other continuous query types. A thorough experimental study with synthetic and real-life data sets verifies the effectiveness of our schemes and demonstrates order-of-magnitude improvements in communication costs compared to the only alternative centralized solution.

2018

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Practical Private Range Search Revisited

Ioannis Demertzis, Odysseas Papapetrou

We consider a data owner that outsources its dataset to an untrusted server. The owner wishes to enable the server to answer range queries on a single attribute, without compromising the privacy of the data and the queries. There are several schemes on "practical" private range search (mainly in Databases venues) that attempt to strike a trade-off between efficiency and security. Nevertheless, these methods either lack provable security guarantees, or permit unacceptable privacy leakages. In this paper, we take an interdisciplinary approach, which combines the rigor of Security formulations and proofs with efficient Data Management techniques. We construct a wide set of novel schemes with realistic security/performance trade-offs, adopting the notion of Searchable Symmetric Encryption (SSE) primarily proposed for keyword search. We reduce range search to multi-keyword search using range covering techniques with tree-like indexes. We demonstrate that, given any secure SSE scheme, the challenge boils down to (i) formulating leakages that arise from the index structure, and (ii) minimizing false positives incurred by some schemes under heavy data skew. We analytically detail the superiority of our proposals over prior work and experimentally confirm their practicality.

2016

Chargement

We consider a data owner that outsources its dataset to an untrusted server. The owner wishes to enable the server to answer range queries on a single attribute, without compromising the privacy of the data and the queries. There are several schemes on “practical” private range search (mainly in database venues) that attempt to strike a trade-off between efficiency and security. Nevertheless, these methods either lack provable security guarantees or permit unacceptable privacy leakages. In this article, we take an interdisciplinary approach, which combines the rigor of security formulations and proofs with efficient data management techniques. We construct a wide set of novel schemes with realistic security/performance trade-offs, adopting the notion of Searchable Symmetric Encryption (SSE), primarily proposed for keyword search. We reduce range search to multi-keyword search using range-covering techniques with tree-like indexes, and formalize the problem as Range Searchable Symmetric Encryption (RSSE). We demonstrate that, given any secure SSE scheme, the challenge boils down to (i) formulating leakages that arise from the index structure and (ii) minimizing false positives incurred by some schemes under heavy data skew. We also explain an important concept in the recent SSE bibliography, namely locality, and design generic and specialized ways to attribute locality to our RSSE schemes. Moreover, we are the first to devise secure schemes for answering range aggregate queries, such as range sums and range min/max. We analytically detail the superiority of our proposals over prior work and experimentally confirm their practicality.

2018

Chargement

Monitoring distributed fragmented skylines

Odysseas Papapetrou

2018