Yodsawalai Chodpathumwan

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This paper shows how to analyze the influences of object characteristics on detection performance and the frequency and impact of different types of false positives. In particular, we examine effects of occlusion, size, aspect ratio, visibility of parts, viewpoint, localization error, and confusion with semantically similar objects, other labeled objects,(More)
Real-world databases often have extremely complex schemas. With thousands of entity types and relationships, each with a hundred or so attributes, it is extremely difficult for new users to explore the data and formulate queries. Schema free query interfaces (SFQIs) address this problem by allowing users with no knowledge of the schema to submit queries. We(More)
Real-world databases often have extremely complex schemas. With thousands of entity types and relationships, each with a hundred or so attributes, it is extremely difficult for new users to explore the data and formulate queries. Schema free query interfaces (SFQIs) address this problem by allowing users with no knowledge of the schema to submit queries. We(More)
It is well established that extracting and annotating occurrences of entities in a collection of unstructured text documents with their concepts improve the effectiveness of answering queries over the collection. However, it is very resource intensive to create and maintain large annotated collections. Since the available resources of an enterprise are(More)
Finding similar entities over data graphs is an important problem with many applications. Current similarity search algorithms use intuitively appealing heuristics that leverage the link information in the data graph to quantify the degree of similarity between its entities. In this paper, using examples from real-world data sets, we show that people(More)
Finding similar or strongly related entities in a graph database is a fundamental problem in data management and analytics with applications in similarity query processing, entity resolution, and pattern matching. Similarity search algorithms usually leverage the structural properties of the data graph to quantify the degree of similarity or relevance(More)
Finding similar entities is a fundamental problem in graph data analysis. Similarity search algorithms usually leverage the structural properties of the database to quantify the degree of similarity between entities. However, the same information can be represented in different structures and the structural properties observed over particular(More)
Graph analytics algorithms leverage quantifiable structural properties of the data to predict interesting concepts and relationships. The same information, however, can be represented using many different structures and the structural properties observed over particular representations do not necessarily hold for alternative structures. Because these(More)
It is known that annotating entities in unstructured and semistructured datasets by their concepts improves the effectiveness of answering queries over these datasets. Ideally, one would like to annotate entities of all relevant concepts in a dataset. However, it takes substantial time and computational resources to annotate concepts in large datasets and(More)
Database analytics algorithms leverage quantifiable structural properties of the data to predict interesting concepts and relationships. The same information, however, can be represented using many different structures and the structural properties observed over particular representations do not necessarily hold for alternative structures. Thus, there is no(More)