360,107 research outputs found
Learning Residual Finite-State Automata Using Observation Tables
We define a two-step learner for RFSAs based on an observation table by using
an algorithm for minimal DFAs to build a table for the reversal of the language
in question and showing that we can derive the minimal RFSA from it after some
simple modifications. We compare the algorithm to two other table-based ones of
which one (by Bollig et al. 2009) infers a RFSA directly, and the other is
another two-step learner proposed by the author. We focus on the criterion of
query complexity.Comment: In Proceedings DCFS 2010, arXiv:1008.127
The Usefulness of Multilevel Hash Tables with Multiple Hash Functions in Large Databases
In this work, attempt is made to select three good hash functions which uniformly distribute hash values that permute their internal states and allow the input bits to generate different output bits. These functions are used in different levels of hash tables that are coded in Java Programming Language and a quite number of data records serve as primary data for testing the performances. The result shows that the two-level hash tables with three different hash functions give a superior performance over one-level hash table with two hash functions or zero-level hash table with one function in term of reducing the conflict keys and quick look-up for a particular element. The result assists to reduce the complexity of join operation in query language from O(n2) to O(1) by placing larger query result, if any, in multilevel hash tables with multiple hash functions and generate shorter query result
On-the-fly Table Generation
Many information needs revolve around entities, which would be better
answered by summarizing results in a tabular format, rather than presenting
them as a ranked list. Unlike previous work, which is limited to retrieving
existing tables, we aim to answer queries by automatically compiling a table in
response to a query. We introduce and address the task of on-the-fly table
generation: given a query, generate a relational table that contains relevant
entities (as rows) along with their key properties (as columns). This problem
is decomposed into three specific subtasks: (i) core column entity ranking,
(ii) schema determination, and (iii) value lookup. We employ a feature-based
approach for entity ranking and schema determination, combining deep semantic
features with task-specific signals. We further show that these two subtasks
are not independent of each other and can assist each other in an iterative
manner. For value lookup, we combine information from existing tables and a
knowledge base. Using two sets of entity-oriented queries, we evaluate our
approach both on the component level and on the end-to-end table generation
task.Comment: The 41st International ACM SIGIR Conference on Research and
Development in Information Retrieva
Structurally Tractable Uncertain Data
Many data management applications must deal with data which is uncertain,
incomplete, or noisy. However, on existing uncertain data representations, we
cannot tractably perform the important query evaluation tasks of determining
query possibility, certainty, or probability: these problems are hard on
arbitrary uncertain input instances. We thus ask whether we could restrict the
structure of uncertain data so as to guarantee the tractability of exact query
evaluation. We present our tractability results for tree and tree-like
uncertain data, and a vision for probabilistic rule reasoning. We also study
uncertainty about order, proposing a suitable representation, and study
uncertain data conditioned by additional observations.Comment: 11 pages, 1 figure, 1 table. To appear in SIGMOD/PODS PhD Symposium
201
IVOA Recommendation: Table Access Protocol Version 1.0
The table access protocol (TAP) defines a service protocol for accessing
general table data, including astronomical catalogs as well as general database
tables. Access is provided for both database and table metadata as well as for
actual table data. This version of the protocol includes support for multiple
query languages, including queries specified using the Astronomical Data Query
Language (ADQL [1]) and the Parameterised Query Language (PQL, under
development) within an integrated interface. It also includes support for both
synchronous and asynchronous queries. Special support is provided for spatially
indexed queries using the spatial extensions in ADQL. A multi-position query
capability permits queries against an arbitrarily large list of astronomical
targets, providing a simple spatial cross-matching capability. More
sophisticated distributed cross-matching capabilities are possible by
orchestrating a distributed query across multiple TAP services
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