82 research outputs found
Which Conference Is That? A Case Study in Computer Science
Conferences play a major role in some disciplines such as computer science and are often used in research quality evaluation exercises. Differently from journals and books, for which ISSN and ISBN codes provide unambiguous keys, recognizing the conference series in which a paper was published is a rather complex endeavor: There is no unique code assigned to conferences, and the way their names are written may greatly vary across years and catalogs. In this article, we propose a technique for the entity resolution of conferences based on the analysis of different semantic parts of their names. We present the results of an investigation of our technique on a dataset of 42,395 distinct computer science conference names excerpted from the DBLP computer science repository,1 which we automatically link to different authority files. With suitable data cleaning, the precision of our record linkage algorithm can be as high as 94%. A comparison with results obtainable using state-of-the-art general-purpose record linkage algorithms rounds off the article, showing that our ad hoc solution largely outperforms them in terms of the quality of the results
FlashProfile: A Framework for Synthesizing Data Profiles
We address the problem of learning a syntactic profile for a collection of
strings, i.e. a set of regex-like patterns that succinctly describe the
syntactic variations in the strings. Real-world datasets, typically curated
from multiple sources, often contain data in various syntactic formats. Thus,
any data processing task is preceded by the critical step of data format
identification. However, manual inspection of data to identify the different
formats is infeasible in standard big-data scenarios.
Prior techniques are restricted to a small set of pre-defined patterns (e.g.
digits, letters, words, etc.), and provide no control over granularity of
profiles. We define syntactic profiling as a problem of clustering strings
based on syntactic similarity, followed by identifying patterns that succinctly
describe each cluster. We present a technique for synthesizing such profiles
over a given language of patterns, that also allows for interactive refinement
by requesting a desired number of clusters.
Using a state-of-the-art inductive synthesis framework, PROSE, we have
implemented our technique as FlashProfile. Across tasks over large
real datasets, we observe a median profiling time of only s.
Furthermore, we show that access to syntactic profiles may allow for more
accurate synthesis of programs, i.e. using fewer examples, in
programming-by-example (PBE) workflows such as FlashFill.Comment: 28 pages, SPLASH (OOPSLA) 201
Temporal Stream Logic: Synthesis beyond the Bools
Reactive systems that operate in environments with complex data, such as
mobile apps or embedded controllers with many sensors, are difficult to
synthesize. Synthesis tools usually fail for such systems because the state
space resulting from the discretization of the data is too large. We introduce
TSL, a new temporal logic that separates control and data. We provide a
CEGAR-based synthesis approach for the construction of implementations that are
guaranteed to satisfy a TSL specification for all possible instantiations of
the data processing functions. TSL provides an attractive trade-off for
synthesis. On the one hand, synthesis from TSL, unlike synthesis from standard
temporal logics, is undecidable in general. On the other hand, however,
synthesis from TSL is scalable, because it is independent of the complexity of
the handled data. Among other benchmarks, we have successfully synthesized a
music player Android app and a controller for an autonomous vehicle in the Open
Race Car Simulator (TORCS.
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