Yong-Ju Lee

A Learning Ontology Method for RESTful Semantic Web Services

The growing number of Restful web services available on the web raises a challenging search problem as to how the desired web services should be located. Traditional keyword searching is inaccurate, and its limitations have been noted for several years. We propose a combination method of WADL and a learning ontology mechanism to enable Restful semantic web services. These syntactic and semantic descriptions allow search engines to support a similarity search for Restful web services. We describe an experimental study on a collection of 168 Restful web services. The experimental results show that our method has higher performance in terms both of the rate of recall and precision compared to existing methods.

Early separation of filter and refinement steps in spatial query optimization

The spatial query has been processed in two steps, the filter step and the refinement step, due to the large volume and high complexity of the spatial data. However, this approach has been considered only in the query execution phase after completion of the query optimization phase. This paper presents query optimization strategies which take the characteristics of spatial databases into account. The first strategy is the separation of filter and refinement steps not in the query execution phase but in the query optimization phase. As the second strategy, several refinement operations can be combined in processing a complex query, and as the third strategy several filter operations can also be combined. We call the optimization technique utilizing these strategies the early separated filter and refinement (ESFAR). This paper also presents a rule-based optimization technique for ESFAR.

Spatial query optimization utilizing early separated filter and refinement strategy

Abstract Due to the high complexity and large volume of spatial data, a spatial query is usually processed in two steps, called the filter step and the refinement step . However, the two-step processing of the spatial query has been considered locally in one spatial predicate evaluation at the query execution level. This paper presents query optimization strategies which exploit the two-step processing of a spatial query at the query optimization level. The first strategy involves the separation of filter and refinement steps not in the query execution phase but in the query optimization phase. As the second strategy, several refinement operations can be combined in processing a complex query if they were already separated, and as the third strategy several filter operations can also be combined. We call the optimization technique utilizing these strategies the Early Separated Filter And Refinement (ESFAR). This paper also presents an algebra, which is called the Intermediate Spatial Object Algebra (ISOA), and optimization rules for ESFAR. Through experiments using real data, we compare the ESFAR optimization technique with a traditional optimization technique which does not separate filter and refinement steps from the query optimization phase. The experimental results show that the ESFAR optimization technique generates more efficient query execution plans than the traditional one in many cases.

Automatic Web API Composition for Semantic Data Mashups

Data mashups enable users to create new applications by combining Web APIs from several data sources. Although data mashups have become very popular over the last few years, there are several challenging issues when combining Web APIs into data mashups, especially when compatible APIs are manually discovered and composed by mash up developers. In this paper, we propose an approach for automatic discovery and composition of Web APIs using their semantic descriptions. This approach can be described as that of generating directed a cyclic graphs (DAGs) that can produce output satisfying a desired goal. We rapidly filter out APIs that are guaranteed not to involve the composition in order to produce the DAGs efficiently.

Building semantic ontologies for RESTful web services

The growing number of RESTful web services available on the web raises a challenging search problem. We propose a combination method of WADL and a learning ontology mechanism to enable RESTful semantic web services. These syntactic and semantic descriptions allow search engines to support a similarity search for RESTful web services.

Analysis of two-step index structure for complex spatial objects

Abstract An efficient index structure for complex spatial objects is one of the most challenging requirements in non-traditional applications such as geographic information systems (GISs), computer-aided design (CAD), and multimedia databases. In this paper we first propose an extension of an existing index structure called the two-step index structure (TSIS). The TSIS integrates two index structures, one for original objects and the other for their decomposed components. Then, we present a cost model that predicts the performance of the TSIS. In contrast to several earlier investigations on this subject which only considered the filter step, we take into account the performance of the refinement step. Experimental results show that the cost model is accurate, the relative error being below 15%. The performance of our index structure is compared with that of a state-of-the-art index structure by experimental measurements. Our index structure outperforms the state-of-the-art index structure due to its ability to reduce a large amount of storage.

Semantic-Based Web API Composition for Data Mashups

With the growing popularity of data mashups, the number of Web APIs has increased significantly. As a result, finding and composing the right APIs has become an increasingly complex task. Although several tools such as Yahoos Pipes, IBMs Lotus Mashup, and Intels Mashmaker have been developed to enable users to create data mashups without programming skills, there are several challenging issues when combining a large number of APIs into the data mashup. This paper proposes novel algorithms for the automatic discovery and composition of Web APIs. Our discovery algorithm adopts strategies that rapidly prune APIs that are guaranteed not to match the query. Our composition algorithm consists of constructing a composable similarity graph (CSG) and searching composition candidates. The CSG presents the semantic functional dependency between the inputs and the outputs of the Web APIs. Using this graph, we generate directed acyclic graphs (DAGs) that can produce the output satisfying the desired goal. We evaluate the algorithms on a real-world dataset from ProgrammableWeb.com, and show that they can produce the results satisfying the users desired output.

Controlled Decomposition Strategy for Complex Spatial Objects

The efficient query processing for complex spatial objects is one of most challenging requirements in many non-traditional applications such as geographic information systems, computer-aided design and multimedia databases. The performance of spatial query processing can be improved by decomposing a complex object into a small number of simple components. This paper investigates a natural trade-off between the number and the complexity of decomposed components. In particular, we propose a new object decomposition method which can control the number of components using a parameter. The proposed method is able to finetune the trade-off by controlling the parameter. An optimal value of the parameter is explored through experimental measurements. The decomposition method with this optimal value outperforms traditional decomposition methods. The gain by applying the optimal value is more clear as the complexity of spatial objects increases.

Spatial query processing using object decomposition method

We propose a new object decomposition method, called DMBRs, to improve the performance of spatial query processing. This method is suitable for complex spatial objects in real-world geographic applications. The basic idea is that a polygon is recursively divided into two subpolygons by splitting its MBR until a given constraint is satisfied. To increase the efficiency of the DMBRs method, an extension of an existing spatial indexing structure is presented. Since this new structure can prune a number of false hits quickly, the performance of spatial query processing can be improved. The proposed method is compared with traditional decomposition methods by an analytical study. This comparison shows that our decomposition method outperforms the traditional decomposition methods.

Two-step RDF query processing for Linked Data

Since RDF triples are modeled as graphs, we cannot directly adopt existing solutions from relational databases and XML technologies. Thus, there are still a number of open problems in the area of Linked Data. We present a hybrid method between centralized and distributed approaches. By using auxiliary indexes based on the MBB approximation, our approach can retrieve distributed Linked Data efficiently. The goal of our approach is to support efficient join query processing by quickly pruning unnecessary scanning data.