189 research outputs found
On the Complexity of Mining Itemsets from the Crowd Using Taxonomies
We study the problem of frequent itemset mining in domains where data is not
recorded in a conventional database but only exists in human knowledge. We
provide examples of such scenarios, and present a crowdsourcing model for them.
The model uses the crowd as an oracle to find out whether an itemset is
frequent or not, and relies on a known taxonomy of the item domain to guide the
search for frequent itemsets. In the spirit of data mining with oracles, we
analyze the complexity of this problem in terms of (i) crowd complexity, that
measures the number of crowd questions required to identify the frequent
itemsets; and (ii) computational complexity, that measures the computational
effort required to choose the questions. We provide lower and upper complexity
bounds in terms of the size and structure of the input taxonomy, as well as the
size of a concise description of the output itemsets. We also provide
constructive algorithms that achieve the upper bounds, and consider more
efficient variants for practical situations.Comment: 18 pages, 2 figures. To be published to ICDT'13. Added missing
acknowledgemen
Labeling Workflow Views with Fine-Grained Dependencies
This paper considers the problem of efficiently answering reachability
queries over views of provenance graphs, derived from executions of workflows
that may include recursion. Such views include composite modules and model
fine-grained dependencies between module inputs and outputs. A novel
view-adaptive dynamic labeling scheme is developed for efficient query
evaluation, in which view specifications are labeled statically (i.e. as they
are created) and data items are labeled dynamically as they are produced during
a workflow execution. Although the combination of fine-grained dependencies and
recursive workflows entail, in general, long (linear-size) data labels, we show
that for a large natural class of workflows and views, labels are compact
(logarithmic-size) and reachability queries can be evaluated in constant time.
Experimental results demonstrate the benefit of this approach over the
state-of-the-art technique when applied for labeling multiple views.Comment: VLDB201
Towards Tractable Algebras for Bags
AbstractBags, i.e., sets with duplicates, are often used to implement relations in database systems. In this paper, we study the expressive power of algebras for manipulating bags. The algebra we present is a simple extension of the nested relation algebra. Our aim is to investigate how the use of bags in the language extends its expressive power and increases its complexity. We consider two main issues, namely (i) the impact of the depth of bag nesting on the expressive power and (ii) the complexity and the expressive power induced by the algebraic operations. We show that the bag algebra is more expressive than the nested relation algebra (at all levels of nesting), and that the difference may be subtle. We establish a hierarchy based on the structure of algebra expressions. This hierarchy is shown to be highly related to the properties of the powerset operator
Answering Regular Path Queries on Workflow Provenance
This paper proposes a novel approach for efficiently evaluating regular path
queries over provenance graphs of workflows that may include recursion. The
approach assumes that an execution g of a workflow G is labeled with
query-agnostic reachability labels using an existing technique. At query time,
given g, G and a regular path query R, the approach decomposes R into a set of
subqueries R1, ..., Rk that are safe for G. For each safe subquery Ri, G is
rewritten so that, using the reachability labels of nodes in g, whether or not
there is a path which matches Ri between two nodes can be decided in constant
time. The results of each safe subquery are then composed, possibly with some
small unsafe remainder, to produce an answer to R. The approach results in an
algorithm that significantly reduces the number of subqueries k over existing
techniques by increasing their size and complexity, and that evaluates each
subquery in time bounded by its input and output size. Experimental results
demonstrate the benefit of this approach
Provenance Views for Module Privacy
Scientific workflow systems increasingly store provenance information about
the module executions used to produce a data item, as well as the parameter
settings and intermediate data items passed between module executions. However,
authors/owners of workflows may wish to keep some of this information
confidential. In particular, a module may be proprietary, and users should not
be able to infer its behavior by seeing mappings between all data inputs and
outputs. The problem we address in this paper is the following: Given a
workflow, abstractly modeled by a relation R, a privacy requirement \Gamma and
costs associated with data. The owner of the workflow decides which data
(attributes) to hide, and provides the user with a view R' which is the
projection of R over attributes which have not been hidden. The goal is to
minimize the cost of hidden data while guaranteeing that individual modules are
\Gamma -private. We call this the "secureview" problem. We formally define the
problem, study its complexity, and offer algorithmic solutions
Top-k Querying of Unknown Values under Order Constraints
Many practical scenarios make it necessary to evaluate top-k queries over data items with partially unknown values. This paper considers a setting where the values are taken from a numerical domain, and where some partial order constraints are given over known and unknown values: under these constraints, we assume that all possible worlds are equally likely.
Our work is the first to propose a principled scheme to derive the value distributions and expected values of unknown items in this setting, with the goal of computing estimated top-k results by interpolating the unknown values from the known ones. We study the complexity of this general task, and show tight complexity bounds, proving that the problem is intractable, but
can be tractably approximated. We then consider the case of tree-shaped partial orders, where we show a constructive PTIME solution. We also compare our problem setting to other top-k definitions on uncertain data
Filtering With the Crowd: CrowdScreen Revisited
Filtering a set of items, based on a set of properties that can be verified by humans, is a common application of CrowdSourcing. When the workers are error-prone, each item is presented to multiple users, to limit the probability of misclassification. Since the Crowd is a relatively expensive resource, minimizing the number of questions per item may naturally result in big savings. Several algorithms to address this minimization problem have been presented in the CrowdScreen framework by Parameswaran et al. However, those algorithms do not scale well and therefore cannot be used in scenarios where high accuracy is required in spite of high user error rates. The goal of this paper is thus to devise algorithms that can cope with such situations. To achieve this, we provide new theoretical insights to the problem, then use them to develop a new efficient algorithm. We also propose novel optimizations for the algorithms of CrowdScreen that improve their scalability. We complement our theoretical study by an experimental evaluation of the algorithms on a large set of synthetic parameters as well as real-life crowdsourcing scenarios, demonstrating the advantages of our solution
Just in Time: Personal Temporal Insights for Altering Model Decisions
The interpretability of complex Machine Learning models is coming to be a
critical social concern, as they are increasingly used in human-related
decision-making processes such as resume filtering or loan applications.
Individuals receiving an undesired classification are likely to call for an
explanation -- preferably one that specifies what they should do in order to
alter that decision when they reapply in the future. Existing work focuses on a
single ML model and a single point in time, whereas in practice, both models
and data evolve over time: an explanation for an application rejection in 2018
may be irrelevant in 2019 since in the meantime both the model and the
applicant's data can change. To this end, we propose a novel framework that
provides users with insights and plans for changing their classification in
particular future time points. The solution is based on combining
state-of-the-art algorithms for (single) model explanations, ones for
predicting future models, and database-style querying of the obtained
explanations. We propose to demonstrate the usefulness of our solution in the
context of loan applications, and interactively engage the audience in
computing and viewing suggestions tailored for applicants based on their unique
characteristic
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