4,239 research outputs found
Building Confidential and Efficient Query Services in the Cloud with RASP Data Perturbation
With the wide deployment of public cloud computing infrastructures, using
clouds to host data query services has become an appealing solution for the
advantages on scalability and cost-saving. However, some data might be
sensitive that the data owner does not want to move to the cloud unless the
data confidentiality and query privacy are guaranteed. On the other hand, a
secured query service should still provide efficient query processing and
significantly reduce the in-house workload to fully realize the benefits of
cloud computing. We propose the RASP data perturbation method to provide secure
and efficient range query and kNN query services for protected data in the
cloud. The RASP data perturbation method combines order preserving encryption,
dimensionality expansion, random noise injection, and random projection, to
provide strong resilience to attacks on the perturbed data and queries. It also
preserves multidimensional ranges, which allows existing indexing techniques to
be applied to speedup range query processing. The kNN-R algorithm is designed
to work with the RASP range query algorithm to process the kNN queries. We have
carefully analyzed the attacks on data and queries under a precisely defined
threat model and realistic security assumptions. Extensive experiments have
been conducted to show the advantages of this approach on efficiency and
security.Comment: 18 pages, to appear in IEEE TKDE, accepted in December 201
Diamond Dicing
In OLAP, analysts often select an interesting sample of the data. For
example, an analyst might focus on products bringing revenues of at least 100
000 dollars, or on shops having sales greater than 400 000 dollars. However,
current systems do not allow the application of both of these thresholds
simultaneously, selecting products and shops satisfying both thresholds. For
such purposes, we introduce the diamond cube operator, filling a gap among
existing data warehouse operations.
Because of the interaction between dimensions the computation of diamond
cubes is challenging. We compare and test various algorithms on large data sets
of more than 100 million facts. We find that while it is possible to implement
diamonds in SQL, it is inefficient. Indeed, our custom implementation can be a
hundred times faster than popular database engines (including a row-store and a
column-store).Comment: 29 page
Scalable Model-Based Management of Correlated Dimensional Time Series in ModelarDB+
To monitor critical infrastructure, high quality sensors sampled at a high
frequency are increasingly used. However, as they produce huge amounts of data,
only simple aggregates are stored. This removes outliers and fluctuations that
could indicate problems. As a remedy, we present a model-based approach for
managing time series with dimensions that exploits correlation in and among
time series. Specifically, we propose compressing groups of correlated time
series using an extensible set of model types within a user-defined error bound
(possibly zero). We name this new category of model-based compression methods
for time series Multi-Model Group Compression (MMGC). We present the first MMGC
method GOLEMM and extend model types to compress time series groups. We propose
primitives for users to effectively define groups for differently sized data
sets, and based on these, an automated grouping method using only the time
series dimensions. We propose algorithms for executing simple and
multi-dimensional aggregate queries on models. Last, we implement our methods
in the Time Series Management System (TSMS) ModelarDB (ModelarDB+). Our
evaluation shows that compared to widely used formats, ModelarDB+ provides up
to 13.7 times faster ingestion due to high compression, 113 times better
compression due to the adaptivity of GOLEMM, 630 times faster aggregates by
using models, and close to linear scalability. It is also extensible and
supports online query processing.Comment: 12 Pages, 28 Figures, and 1 Tabl
Collaborative OLAP with Tag Clouds: Web 2.0 OLAP Formalism and Experimental Evaluation
Increasingly, business projects are ephemeral. New Business Intelligence
tools must support ad-lib data sources and quick perusal. Meanwhile, tag clouds
are a popular community-driven visualization technique. Hence, we investigate
tag-cloud views with support for OLAP operations such as roll-ups, slices,
dices, clustering, and drill-downs. As a case study, we implemented an
application where users can upload data and immediately navigate through its ad
hoc dimensions. To support social networking, views can be easily shared and
embedded in other Web sites. Algorithmically, our tag-cloud views are
approximate range top-k queries over spontaneous data cubes. We present
experimental evidence that iceberg cuboids provide adequate online
approximations. We benchmark several browser-oblivious tag-cloud layout
optimizations.Comment: Software at https://github.com/lemire/OLAPTagClou
Benchmarking Summarizability Processing in XML Warehouses with Complex Hierarchies
Business Intelligence plays an important role in decision making. Based on
data warehouses and Online Analytical Processing, a business intelligence tool
can be used to analyze complex data. Still, summarizability issues in data
warehouses cause ineffective analyses that may become critical problems to
businesses. To settle this issue, many researchers have studied and proposed
various solutions, both in relational and XML data warehouses. However, they
find difficulty in evaluating the performance of their proposals since the
available benchmarks lack complex hierarchies. In order to contribute to
summarizability analysis, this paper proposes an extension to the XML warehouse
benchmark (XWeB) with complex hierarchies. The benchmark enables us to generate
XML data warehouses with scalable complex hierarchies as well as
summarizability processing. We experimentally demonstrated that complex
hierarchies can definitely be included into a benchmark dataset, and that our
benchmark is able to compare two alternative approaches dealing with
summarizability issues.Comment: 15th International Workshop on Data Warehousing and OLAP (DOLAP
2012), Maui : United States (2012
- …