2,247 research outputs found
Missing values estimation for skylines in incomplete database
Incompleteness of data is a common problem in many databases including web heterogeneous databases, multi-relational databases, spatial and temporal databases and data integration. The incompleteness of data introduces challenges in processing queries as providing accurate results that best meet the query conditions over incomplete database is not a trivial task. Several techniques have been proposed to process queries in incomplete database. Some of these techniques retrieve the query results based on the existing values rather than estimating the missing values. Such techniques are undesirable in many cases as the dimensions with missing values might be the important dimensions of the user’s query. Besides, the output is incomplete and might not satisfy the user preferences. In this paper we propose an approach that estimates missing values in skylines to guide users in selecting the most appropriate skylines from the several candidate skylines. The approach utilizes the concept of mining attribute correlations to generate an Approximate Functional Dependencies (AFDs) that captured the relationships between the dimensions. Besides, identifying the strength of probability correlations to estimate the values. Then, the skylines with estimated values are ranked. By doing so, we ensure that the retrieved skylines are in the order of their estimated precision
The data-exchange chase under the microscope
In this paper we take closer look at recent developments for the chase
procedure, and provide additional results. Our analysis allows us create a
taxonomy of the chase variations and the properties they satisfy. Two of the
most central problems regarding the chase is termination, and discovery of
restricted classes of sets of dependencies that guarantee termination of the
chase. The search for the restricted classes has been motivated by a fairly
recent result that shows that it is undecidable to determine whether the chase
with a given dependency set will terminate on a given instance. There is a
small dissonance here, since the quest has been for classes of sets of
dependencies guaranteeing termination of the chase on all instances, even
though the latter problem was not known to be undecidable. We resolve the
dissonance in this paper by showing that determining whether the chase with a
given set of dependencies terminates on all instances is coRE-complete. For the
hardness proof we use a reduction from word rewriting systems, thereby also
showing the close connection between the chase and word rewriting. The same
reduction also gives us the aforementioned instance-dependent RE-completeness
result as a byproduct. For one of the restricted classes guaranteeing
termination on all instances, the stratified sets dependencies, we provide new
complexity results for the problem of testing whether a given set of
dependencies belongs to it. These results rectify some previous claims that
have occurred in the literature.Comment: arXiv admin note: substantial text overlap with arXiv:1303.668
A model and framework for reliable build systems
Reliable and fast builds are essential for rapid turnaround during
development and testing. Popular existing build systems rely on correct manual
specification of build dependencies, which can lead to invalid build outputs
and nondeterminism. We outline the challenges of developing reliable build
systems and explore the design space for their implementation, with a focus on
non-distributed, incremental, parallel build systems. We define a general model
for resources accessed by build tasks and show its correspondence to the
implementation technique of minimum information libraries, APIs that return no
information that the application doesn't plan to use. We also summarize
preliminary experimental results from several prototype build managers
Social Collaborative Retrieval
Socially-based recommendation systems have recently attracted significant
interest, and a number of studies have shown that social information can
dramatically improve a system's predictions of user interests. Meanwhile, there
are now many potential applications that involve aspects of both recommendation
and information retrieval, and the task of collaborative retrieval---a
combination of these two traditional problems---has recently been introduced.
Successful collaborative retrieval requires overcoming severe data sparsity,
making additional sources of information, such as social graphs, particularly
valuable. In this paper we propose a new model for collaborative retrieval, and
show that our algorithm outperforms current state-of-the-art approaches by
incorporating information from social networks. We also provide empirical
analyses of the ways in which cultural interests propagate along a social graph
using a real-world music dataset.Comment: 10 page
Proceedings of the 2008 Oxford University Computing Laboratory student conference.
This conference serves two purposes. First, the event is a useful pedagogical exercise for all participants, from the conference committee and referees, to the presenters and the audience. For some presenters, the conference may be the first time their work has been subjected to peer-review. For others, the conference is a testing ground for announcing work, which will be later presented at international conferences, workshops, and symposia. This leads to the conference's second purpose: an opportunity to expose the latest-and-greatest research findings within the laboratory. The fourteen abstracts within these proceedings were selected by the programme and conference committee after a round of peer-reviewing, by both students and staff within this department
Explanation-Based Auditing
To comply with emerging privacy laws and regulations, it has become common
for applications like electronic health records systems (EHRs) to collect
access logs, which record each time a user (e.g., a hospital employee) accesses
a piece of sensitive data (e.g., a patient record). Using the access log, it is
easy to answer simple queries (e.g., Who accessed Alice's medical record?), but
this often does not provide enough information. In addition to learning who
accessed their medical records, patients will likely want to understand why
each access occurred. In this paper, we introduce the problem of generating
explanations for individual records in an access log. The problem is motivated
by user-centric auditing applications, and it also provides a novel approach to
misuse detection. We develop a framework for modeling explanations which is
based on a fundamental observation: For certain classes of databases, including
EHRs, the reason for most data accesses can be inferred from data stored
elsewhere in the database. For example, if Alice has an appointment with Dr.
Dave, this information is stored in the database, and it explains why Dr. Dave
looked at Alice's record. Large numbers of data accesses can be explained using
general forms called explanation templates. Rather than requiring an
administrator to manually specify explanation templates, we propose a set of
algorithms for automatically discovering frequent templates from the database
(i.e., those that explain a large number of accesses). We also propose
techniques for inferring collaborative user groups, which can be used to
enhance the quality of the discovered explanations. Finally, we have evaluated
our proposed techniques using an access log and data from the University of
Michigan Health System. Our results demonstrate that in practice we can provide
explanations for over 94% of data accesses in the log.Comment: VLDB201
- …