Tuple

About: Tuple is a research topic. Over the lifetime, 6513 publications have been published within this topic receiving 146057 citations. The topic is also known as: tuple & ordered tuplet.

Combination of classification and regression in decision tree for multi-labeling image annotation and retrieval

Abstract: This paper proposes a semantic-based image retrieval approach which refers to the ability of using keywords for searching within image datasets. This is possible by adding some textual metadata, called image annotation. Combination of classification and regression in decision tree (DT) has been employed for multi-labeling image annotation in which, more than one label will be considered for every single tuple. In the proposed approach, all concepts and their corresponding ranks will be stored in each DT leaf node instead of storing only a concept or a rank. We have used a hierarchical network of semantics to achieve a better performance. The main idea behind our approach is that in each leaf node, the system should give a higher rank to concepts with highest degree of purity and details according to prepared hierarchical semantic network. A segmented, feature extracted and annotated image dataset, SAIAPR-TC12, has been used for evaluation. A hierarchy of 256 semantic concepts which have been used in annotation process, made it very suitable for testing the approach. Experimental results confirmed that our approach illustrates better performance in comparison with single-labeling approaches which only assign one class to every single tuple and only support linear relationship among concepts.

DynaCet: Building Dynamic Faceted Search Systems over Databases

Abstract: Extracting information and insights from large databases is a time-consuming activity and has received considerable research attention recently. In this demo, we present DynaCet - a domain independent system that provides effective minimum-effort based dynamic faceted search solutions over enterprise databases. At every step, Dynacet suggests facets depending on the user response in the previous step. Facets are selected based on their ability to rapidly drill down to the most promising tuples, as well as on the ability of the user to provide desired values for them. The benefits provided include faster access to information stored in databases while taking into consideration the variance in user knowledge and preferences.

A performance study of transitive closure algorithms

Abstract: We present a comprehensive performance evaluation of transitive closure (reachability) algorithms for databases. The study is based upon careful implementations of the algorithms, measures page I/O, and covers algorithms for full transitive closure as well as partial transitive closure (finding all successors of each node in a set of given source nodes). We examine a wide range of acyclic graphs with varying density and “locality” of arcs in the graph. We also consider query parameters such as the selectivity of the query, and system parameters such as the buffer size and the page and successor list replacement policies. We show that significant cost tradeoffs exist between the algorithms in this spectrum and identify the factors that influence the performance of the algorithms.An important aspect of our work is that we measure a number of different cost metrics, giving us a good understanding of the predictive power of these metrics with respect to I/O cost. This is especially significant since metrics such as number of tuples generated or number of successor list operations have been widely used to compare transitive closure algorithms in the literature. Our results strongly suggest that these other metrics cannot be reliability used to predict I/O cost of transitive closure evaluation.

Reverse top-k search using random walk with restart

Abstract: With the increasing popularity of social networks, large volumes of graph data are becoming available. Large graphs are also derived by structure extraction from relational, text, or scientific data (e.g., relational tuple networks, citation graphs, ontology networks, protein-protein interaction graphs). Node-to-node proximity is the key building block for many graph-based applications that search or analyze the data. Among various proximity measures, random walk with restart (RWR) is widely adopted because of its ability to consider the global structure of the whole network. Although RWR-based similarity search has been well studied before, there is no prior work on reverse top-k proximity search in graphs based on RWR. We discuss the applicability of this query and show that its direct evaluation using existing methods on RWR-based similarity search has very high computational and storage demands. To address this issue, we propose an indexing technique, paired with an on-line reverse top-k search algorithm. Our experiments show that our technique is efficient and has manageable storage requirements even when applied on very large graphs.