40 research outputs found
A competitive environment for exploratory query expansion
Most information workers query digital libraries many times a day. Yet people have little opportunity to hone their skills in a controlled environment, or compare their performance with others in an objective way. Conversely, although search engine logs record how users evolve queries, they lack crucial information about the user's intent. This paper describes an environment for exploratory query expansion that pits users against each other and lets them compete, and practice, in their own time and on their own workstation. The system captures query evolution behavior on predetermined information-seeking tasks. It is publicly available, and the code is open source so that others can set up their own competitive environments
NEW DYNAMIC QUERY OPTIMIZATION TECHNIQUE IN RELATIONAL DATABASE MANAGEMENT SYSTEMS
Query optimizer is an important component in the architecture of relational data base management system. This component is responsible for translating user submitted query into an efficient query evolution program which can be executed against the database. The present query evolution existing algorithm tries to find the best possible plan to execute a query with a minimum amount of time using mostly semi accurate statistical information (e.g. sizes of temporary relations, selectivity factors, and availability of resources). It is a static approach for generating optimal or close to optimal execution plan. Which in turn increases the execution cost of the query to reduce the execution cost of the query; I propose a new dynamic query optimization algorithm which is based on greedy dynamic programming algorithm uses randomized strategies and reduces the execution cost of the queries and system resources and also it works efficiently with distributed and centralized databases
Ensuring Query Compatibility with Evolving XML Schemas
During the life cycle of an XML application, both schemas and queries may
change from one version to another. Schema evolutions may affect query results
and potentially the validity of produced data. Nowadays, a challenge is to
assess and accommodate the impact of theses changes in rapidly evolving XML
applications.
This article proposes a logical framework and tool for verifying
forward/backward compatibility issues involving schemas and queries. First, it
allows analyzing relations between schemas. Second, it allows XML designers to
identify queries that must be reformulated in order to produce the expected
results across successive schema versions. Third, it allows examining more
precisely the impact of schema changes over queries, therefore facilitating
their reformulation
DynamicBEV: Leveraging Dynamic Queries and Temporal Context for 3D Object Detection
3D object detection is crucial for applications like autonomous driving and
robotics. While query-based 3D object detection for BEV (Bird's Eye View)
images has seen significant advancements, most existing methods follows the
paradigm of static query. Such paradigm is incapable of adapting to complex
spatial-temporal relationships in the scene. To solve this problem, we
introduce a new paradigm in DynamicBEV, a novel approach that employs dynamic
queries for BEV-based 3D object detection. In contrast to static queries, the
proposed dynamic queries exploit K-means clustering and Top-K Attention in a
creative way to aggregate information more effectively from both local and
distant feature, which enable DynamicBEV to adapt iteratively to complex
scenes. To further boost efficiency, DynamicBEV incorporates a Lightweight
Temporal Fusion Module (LTFM), designed for efficient temporal context
integration with a significant computation reduction. Additionally, a
custom-designed Diversity Loss ensures a balanced feature representation across
scenarios. Extensive experiments on the nuScenes dataset validate the
effectiveness of DynamicBEV, establishing a new state-of-the-art and heralding
a paradigm-level breakthrough in query-based BEV object detection
Towards a Workload for Evolutionary Analytics
Emerging data analysis involves the ingestion and exploration of new data
sets, application of complex functions, and frequent query revisions based on
observing prior query answers. We call this new type of analysis evolutionary
analytics and identify its properties. This type of analysis is not well
represented by current benchmark workloads. In this paper, we present a
workload and identify several metrics to test system support for evolutionary
analytics. Along with our metrics, we present methodologies for running the
workload that capture this analytical scenario.Comment: 10 page
An analysis of search query evolution in document classification and clustering
With the increasing use of data analytics in decision-making processes today, the analysis of document collections for various purposes has become a widely accepted area of research. Document classification and clustering are two intensely investigated and active areas of research due to the complex nature of the problem and its impact on society.
However, many of the popular methods developed to classify and cluster documents with high accuracy lack explanation to end users, which affects the trustworthiness of certain applications among them. Therefore, it is crucial to improve explainable classification and clustering methods.
One approach that has shown promise in this regard is the evolved search query (eSQ), a genetic algorithm (GA)-based approach for classification and clustering. GA-based methods excel at finding highly optimized solutions for complex problems, and eSQ has utilized this capability to develop classification and clustering methods that are also human interpretable.
The primary focus of this study is to analyse the eSQ approach to document classification and clustering with an emphasis on explainability. The investigation covers three perspectives of the eSQ-based methods: explainability, document classification, and document clustering. This thesis presents a taxonomy for classification based on human friendliness, empirical observations on the performance of eSQ classifiers using different feature selection methods, the effectiveness of eSQ classifiers for Sinhala documents, and the performance of eSQ clustering for Sinhala documents.
The research contributes significantly by categorizing popular classification methods using the new taxonomy, integrating feature selection methods into eSQ classifiers, enhancing Apache Lucene by incorporating the Sinhala language with basic pre-processing tools, and improving eSQ hybrid single word clustering methods. Notably, the eSQ-based classification and clustering methods demonstrate superior performance when document categories overlap