40,225 research outputs found
Sets and indices in linear programming modelling and their integration with relational data models
LP models are usually constructed using index sets and data tables which are closely related to the attributes and relations of relational database (RDB) systems. We extend the syntax of MPL, an existing LP modelling language, in order to connect it to a given RDB system. This approach reuses existing modelling and database software, provides a rich modelling environment and achieves model and data independence. This integrated software enables Mathematical Programming to be widely used as a decision support tool by unlocking the data residing in corporate databases
Utilising semantic technologies for intelligent indexing and retrieval of digital images
The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as the exploitation of lexical databases for explicit semantic-based query expansion
PRIME: A System for Multi-lingual Patent Retrieval
Given the growing number of patents filed in multiple countries, users are
interested in retrieving patents across languages. We propose a multi-lingual
patent retrieval system, which translates a user query into the target
language, searches a multilingual database for patents relevant to the query,
and improves the browsing efficiency by way of machine translation and
clustering. Our system also extracts new translations from patent families
consisting of comparable patents, to enhance the translation dictionary
Object-oriented querying of existing relational databases
In this paper, we present algorithms which allow an object-oriented
querying of existing relational databases. Our goal is to provide an improved query
interface for relational systems with better query facilities than SQL. This
seems to be very important since, in real world applications, relational systems
are most commonly used and their dominance will remain in the near future. To
overcome the drawbacks of relational systems, especially the poor query facilities
of SQL, we propose a schema transformation and a query translation algorithm.
The schema transformation algorithm uses additional semantic information to enhance
the relational schema and transform it into a corresponding object-oriented
schema. If the additional semantic information can be deducted from an underlying
entity-relationship design schema, the schema transformation may be done
fully automatically. To query the created object-oriented schema, we use the
Structured Object Query Language (SOQL) which provides declarative query facilities
on objects. SOQL queries using the created object-oriented schema are
much shorter, easier to write and understand and more intuitive than corresponding
S Q L queries leading to an enhanced usability and an improved querying of
the database. The query translation algorithm automatically translates SOQL queries
into equivalent SQL queries for the original relational schema
Contextual Media Retrieval Using Natural Language Queries
The widespread integration of cameras in hand-held and head-worn devices as
well as the ability to share content online enables a large and diverse visual
capture of the world that millions of users build up collectively every day. We
envision these images as well as associated meta information, such as GPS
coordinates and timestamps, to form a collective visual memory that can be
queried while automatically taking the ever-changing context of mobile users
into account. As a first step towards this vision, in this work we present
Xplore-M-Ego: a novel media retrieval system that allows users to query a
dynamic database of images and videos using spatio-temporal natural language
queries. We evaluate our system using a new dataset of real user queries as
well as through a usability study. One key finding is that there is a
considerable amount of inter-user variability, for example in the resolution of
spatial relations in natural language utterances. We show that our retrieval
system can cope with this variability using personalisation through an online
learning-based retrieval formulation.Comment: 8 pages, 9 figures, 1 tabl
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