21,481 research outputs found
A Methodology for integration of heterogeneous databases
Reprint. Reprinted from IEEE transactions on knowledge and data engineering. Vol. 6, no. 6 (Dec. 1994) "December 1994."Includes bibliographical references (p. 932).Supported by the Productivity From Information Technology (PROFIT) Research Initiative at MIT.M.P. Reddy ... [et al.
Errata Corrige on “Modeling and Computing Ternary Projective Relations Between Regions”
We report a corrected version of the algorithms to compute ternary projective relations between regions appeared in E. Clementini and R. Billen, "Modeling and computing ternary projective relations between regions," IEEE Transactions on Knowledge and Data Engineering, vol. 18, pp. 799-814, 2006.Peer reviewe
Resolving Multi-party Privacy Conflicts in Social Media
Items shared through Social Media may affect more than one user's privacy ---
e.g., photos that depict multiple users, comments that mention multiple users,
events in which multiple users are invited, etc. The lack of multi-party
privacy management support in current mainstream Social Media infrastructures
makes users unable to appropriately control to whom these items are actually
shared or not. Computational mechanisms that are able to merge the privacy
preferences of multiple users into a single policy for an item can help solve
this problem. However, merging multiple users' privacy preferences is not an
easy task, because privacy preferences may conflict, so methods to resolve
conflicts are needed. Moreover, these methods need to consider how users' would
actually reach an agreement about a solution to the conflict in order to
propose solutions that can be acceptable by all of the users affected by the
item to be shared. Current approaches are either too demanding or only consider
fixed ways of aggregating privacy preferences. In this paper, we propose the
first computational mechanism to resolve conflicts for multi-party privacy
management in Social Media that is able to adapt to different situations by
modelling the concessions that users make to reach a solution to the conflicts.
We also present results of a user study in which our proposed mechanism
outperformed other existing approaches in terms of how many times each approach
matched users' behaviour.Comment: Authors' version of the paper accepted for publication at IEEE
Transactions on Knowledge and Data Engineering, IEEE Transactions on
Knowledge and Data Engineering, 201
Mining Novel Multivariate Relationships in Time Series Data Using Correlation Networks
In many domains, there is significant interest in capturing novel
relationships between time series that represent activities recorded at
different nodes of a highly complex system. In this paper, we introduce
multipoles, a novel class of linear relationships between more than two time
series. A multipole is a set of time series that have strong linear dependence
among themselves, with the requirement that each time series makes a
significant contribution to the linear dependence. We demonstrate that most
interesting multipoles can be identified as cliques of negative correlations in
a correlation network. Such cliques are typically rare in a real-world
correlation network, which allows us to find almost all multipoles efficiently
using a clique-enumeration approach. Using our proposed framework, we
demonstrate the utility of multipoles in discovering new physical phenomena in
two scientific domains: climate science and neuroscience. In particular, we
discovered several multipole relationships that are reproducible in multiple
other independent datasets and lead to novel domain insights.Comment: This is the accepted version of article submitted to IEEE
Transactions on Knowledge and Data Engineering 201
gMark: Schema-Driven Generation of Graphs and Queries
Massive graph data sets are pervasive in contemporary application domains.
Hence, graph database systems are becoming increasingly important. In the
experimental study of these systems, it is vital that the research community
has shared solutions for the generation of database instances and query
workloads having predictable and controllable properties. In this paper, we
present the design and engineering principles of gMark, a domain- and query
language-independent graph instance and query workload generator. A core
contribution of gMark is its ability to target and control the diversity of
properties of both the generated instances and the generated workloads coupled
to these instances. Further novelties include support for regular path queries,
a fundamental graph query paradigm, and schema-driven selectivity estimation of
queries, a key feature in controlling workload chokepoints. We illustrate the
flexibility and practical usability of gMark by showcasing the framework's
capabilities in generating high quality graphs and workloads, and its ability
to encode user-defined schemas across a variety of application domains.Comment: Accepted in November 2016. URL:
http://ieeexplore.ieee.org/document/7762945/. in IEEE Transactions on
Knowledge and Data Engineering 201
On Optimally Partitioning Variable-Byte Codes
The ubiquitous Variable-Byte encoding is one of the fastest compressed
representation for integer sequences. However, its compression ratio is usually
not competitive with other more sophisticated encoders, especially when the
integers to be compressed are small that is the typical case for inverted
indexes. This paper shows that the compression ratio of Variable-Byte can be
improved by 2x by adopting a partitioned representation of the inverted lists.
This makes Variable-Byte surprisingly competitive in space with the best
bit-aligned encoders, hence disproving the folklore belief that Variable-Byte
is space-inefficient for inverted index compression. Despite the significant
space savings, we show that our optimization almost comes for free, given that:
we introduce an optimal partitioning algorithm that does not affect indexing
time because of its linear-time complexity; we show that the query processing
speed of Variable-Byte is preserved, with an extensive experimental analysis
and comparison with several other state-of-the-art encoders.Comment: Published in IEEE Transactions on Knowledge and Data Engineering
(TKDE), 15 April 201
- …