237 research outputs found
Search and Result Presentation in Scientific Workflow Repositories
We study the problem of searching a repository of complex hierarchical
workflows whose component modules, both composite and atomic, have been
annotated with keywords. Since keyword search does not use the graph structure
of a workflow, we develop a model of workflows using context-free bag grammars.
We then give efficient polynomial-time algorithms that, given a workflow and a
keyword query, determine whether some execution of the workflow matches the
query. Based on these algorithms we develop a search and ranking solution that
efficiently retrieves the top-k grammars from a repository. Finally, we propose
a novel result presentation method for grammars matching a keyword query, based
on representative parse-trees. The effectiveness of our approach is validated
through an extensive experimental evaluation
Rule-Based Application Development using Webdamlog
We present the WebdamLog system for managing distributed data on the Web in a
peer-to-peer manner. We demonstrate the main features of the system through an
application called Wepic for sharing pictures between attendees of the sigmod
conference. Using Wepic, the attendees will be able to share, download, rate
and annotate pictures in a highly decentralized manner. We show how WebdamLog
handles heterogeneity of the devices and services used to share data in such a
Web setting. We exhibit the simple rules that define the Wepic application and
show how to easily modify the Wepic application.Comment: SIGMOD - Special Interest Group on Management Of Data (2013
Taming Technical Bias in Machine Learning Pipelines
Machine Learning (ML) is commonly used to automate decisions in domains as varied as credit and lending, medical diagnosis, and hiring. These decisions are consequential, imploring us to carefully balance the benefits of efficiency with the potential risks. Much of the conversation about the risks centers around bias — a term that is used by the technical community ever more frequently but that is still poorly understood. In this paper we focus on technical bias — a type of bias that has so far received limited attention and that the data engineering community is well-equipped to address. We discuss dimensions of technical bias that can arise through the ML lifecycle, particularly when it’s due to preprocessing decisions or post-deployment issues. We present results of our recent work, and discuss future research directions. Our over-all goal is to support the development of systems that expose the knobs of responsibility to data scientists, allowing them to detect instances of technical bias and to mitigate it when possible
A Nutritional Label for Rankings
Algorithmic decisions often result in scoring and ranking individuals to
determine credit worthiness, qualifications for college admissions and
employment, and compatibility as dating partners. While automatic and seemingly
objective, ranking algorithms can discriminate against individuals and
protected groups, and exhibit low diversity. Furthermore, ranked results are
often unstable --- small changes in the input data or in the ranking
methodology may lead to drastic changes in the output, making the result
uninformative and easy to manipulate. Similar concerns apply in cases where
items other than individuals are ranked, including colleges, academic
departments, or products.
In this demonstration we present Ranking Facts, a Web-based application that
generates a "nutritional label" for rankings. Ranking Facts is made up of a
collection of visual widgets that implement our latest research results on
fairness, stability, and transparency for rankings, and that communicate
details of the ranking methodology, or of the output, to the end user. We will
showcase Ranking Facts on real datasets from different domains, including
college rankings, criminal risk assessment, and financial services.Comment: 4 pages, SIGMOD demo, 3 figuress, ACM SIGMOD 201
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Schema Polynomials and Applications
Conceptual complexity is emerging as a new bottleneck as database developers, application developers, and database administrators struggle to design and comprehend large, complex schemas. The simplicity and conciseness of a schema depends critically on the idioms available to express the schema. We propose a formal conceptual schema representation language that combines different design formalisms, and allows schema manipulation that exposes the strengths of each of these formalisms. We demonstrate how the schema factorization framework can be used to generate relational, object-oriented, and faceted physical schemas, allowing a wider exploration of physical schema alternatives than traditional methodologies. We illustrate the potential practical benefits of schema factorization by showing that simple heuristics can significantly reduce the size of a real-world schema description. We also propose the use of schema polynomials to model and derive alternative representations for complex relationships with constraints
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