36,856 research outputs found
Querying ontology using keywords and quantitative restriction phrases
Many approaches for converting keyword queries to formal query languages are presented for natural language interfaces to ontologies. Some approaches present fixed formal query templates, so they lack in providing support with increasing number of words in the user query. Other approaches work on constructing and manipulating subgraphs from RDF graphs so their processing is complex with respect to time and space. Techniques are presented to perform operations by obtaining a reduced RDF graph but they limit the input to some type of resources so their complete complexity with all type of input resources is unknown. For formal query generation, we present a variable query template whose computation is facilitated by less complex and distributed RDF property and relation graphs. A prototype QuriOnto is developed to evaluate our design. The user can query QuriOnto with any number of words and resource types. Also, to the best of our knowledge, it is the first system that can handle quantitative restrictions with keyword queries. As QuriOnto has no support for semantic similarity at this time except for rdfs labels so its recall is low but high precision shows that the approach is promising for the generation of corresponding formal queries
Query Flattening and the Nested Data Parallelism Paradigm
This work is based on the observation that languages for two seemingly distant
domains are closely related. Orthogonal query languages based on comprehension
syntax admit various forms of query nesting to construct nested query results
and express complex predicates. Languages for nested data parallelism allow to
nest parallel iterators and thereby admit the parallel evaluation of
computations that are themselves parallel. Both kinds of languages center around
the application of side-effect-free functions to each element of a collection.
The motivation for this work is the seamless integration of relational database
queries with programming languages. In frameworks for language-integrated
database queries, a host language's native collection-programming API is used
to express queries. To mediate between native collection programming and
relational queries, we define an expressive, orthogonal query calculus that
supports nesting and order. The challenge of query flattening is to translate
this calculus to bundles of efficient relational queries restricted to flat,
unordered multisets. Prior approaches to query flattening either support only
query languages that lack in expressiveness or employ a complex, monolithic
translation that is hard to comprehend and generates inefficient code that is
hard to optimize.
To improve on those approaches, we draw on the similarity to nested data
parallelism. Blelloch's flattening transformation is a static program
transformation that translates nested data parallelism to flat data parallel
programs over flat arrays. Based on the flattening transformation, we describe a
pipeline of small, comprehensible lowering steps that translates our nested
query calculus to a bundle of relational queries. The pipeline is based on a
number of well-defined intermediate languages. Our translation adopts the key
concepts of the flattening transformation but is designed with specifics of
relational query processing in mind.
Based on this translation, we revisit all aspects of query flattening. Our
translation is fully compositional and can translate any term of the input
language. Like prior work, the translation by itself produces inefficient code
due to compositionality that is not fit for execution without optimization. In
contrast to prior work, we show that query optimization is orthogonal to
flattening and can be performed before flattening. We employ well-known work on
logical query optimization for nested query languages and demonstrate that this
body of work integrates well with our approach.
Furthermore, we describe an improved encoding of ordered and nested collections
in terms of flat, unordered multisets. Our approach emits idiomatic relational
queries in which the effort required to maintain the non-relational semantics of
the source language (order and nesting) is minimized.
A set of experiments provides evidence that our approach to query flattening can
handle complex, list-based queries with nested results and nested intermediate
data well. We apply our approach to a number of flat and nested benchmark
queries and compare their runtime with hand-written SQL queries. In these
experiments, our SQL code generated from a list-based nested query language
usually performs as well as hand-written queries
Query Formulation and Recommendation for Relational Databases Using User Sessions and Collaborative Filtering
Structured Query Language (SQL) has a uniform structure over different programming languages. The queries fired on Database Management System (DBMS) contain textual information along with selected segments of data parsed by data base management system to fire it as a structured query. Currently DBA needs to execute complex queries on large databases. Many times user or DBA fires similar queries on database server to get useful information. The queries which are similar to each other can then be categorized into two types a) the tuples retrieved by SQL queries are similar b) the fragment of the queries are similar. System gives recommendation to those similar queries so that it saves the time of DBA to construct it again and again. Query suggestions given to DBA or users are known as Query Recommendation. To develop a Query Recommendation system many authors suggested the use of Query Log. Query suggestions are divided into two areas mainly Collaborative Recommendations and Single Log Recommendations. This system is designed by single or collaborative log using parameter known as mixing factor. In this paper we analyzed Sql query Recommendation concepts and their uses. There are basically two types of similarity measure for Query Recommendation considered in [1] such as 1) Fragment Based 2) Tuple Based. Here in this research paper we are motivated towards generating recommendations for nested SQL queries. We adopt hierarchical classification on query log to create classes of similar queries and further to generate recommendations for SQL Query we proceed with finding matching class from which the recommendations can be modeled.
DOI: 10.17762/ijritcc2321-8169.15070
Efficient Retrieval of Similar Time Sequences Using DFT
We propose an improvement of the known DFT-based indexing technique for fast
retrieval of similar time sequences. We use the last few Fourier coefficients
in the distance computation without storing them in the index since every
coefficient at the end is the complex conjugate of a coefficient at the
beginning and as strong as its counterpart. We show analytically that this
observation can accelerate the search time of the index by more than a factor
of two. This result was confirmed by our experiments, which were carried out on
real stock prices and synthetic data
Towards Analytics Aware Ontology Based Access to Static and Streaming Data (Extended Version)
Real-time analytics that requires integration and aggregation of
heterogeneous and distributed streaming and static data is a typical task in
many industrial scenarios such as diagnostics of turbines in Siemens. OBDA
approach has a great potential to facilitate such tasks; however, it has a
number of limitations in dealing with analytics that restrict its use in
important industrial applications. Based on our experience with Siemens, we
argue that in order to overcome those limitations OBDA should be extended and
become analytics, source, and cost aware. In this work we propose such an
extension. In particular, we propose an ontology, mapping, and query language
for OBDA, where aggregate and other analytical functions are first class
citizens. Moreover, we develop query optimisation techniques that allow to
efficiently process analytical tasks over static and streaming data. We
implement our approach in a system and evaluate our system with Siemens turbine
data
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