Comparison of Clustering Algorithm

In this section, we compare clustering algorithms for schema summary. In addi- tion to the proposed weighted k-center clustering algorithm using an influence function(Clusts), we implement the following clustering algorithms:

– Clustc, A community detection based schema summarization approach pro- posed in [6].

– Clustd, the schema summarization approach proposed in [7]. The clustering approach uses a table importance metric based weighted k-center clustering algorithm.

– Clustv, Combines results from clustering using reference similarity and clus- tering using document similarity using a voting scheme similar to [8]. This algorithm focuses on combining clustering from different similarity models rather than combining similarity models.

Figure 6 shows the clustering accuracy achieved for k = (2, 3, 4) for various clustering algorithms. We observed that clustsand clustdachieve almost similar accuracy, with clustsgiving slightly higher accuracy as it was able to successfully cluster the table trade request. If no active transactions are considered for the TPCE database, the table trade request is empty and data oriented approaches

2 3 4 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

k

acc

Fig. 6. Clustering accuracy for different clustering algorithms

are unable to classify the table. For the clustv and clustc approaches, no spe- cific patterns were observed. The reason for low accuracy of clustv is because referential similarity provides a very imbalanced and ineffective clustering which deters the overall clustering accuracy in the voting scheme significantly.

5

Conclusions and Future Work

Schema summarization has been proposed in the literature to help users in exploring complex database schema. Existing approaches for schema summa- rization are data-oriented. In this paper, we proposed a schema summarization approach for relational databases using database schema and the database doc- umentation. We proposed a combined similarity measure to incorporate similar- ities from both sources and proposed a framework for summary generation. Ex- periments were conducted on a benchmark database and the results showed that the proposed approach is as effective as the existing data oriented approaches. We plan to extend this work by developing algorithms for learning the values of various parameters used in the proposed approach.

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Ullas Nambiar1_{, Tanveer Faruquie}2_{, Shamanth Kumar}3_{, Fred Morstatter}3_,

and Huan Liu3

1 _{EMC India COE, Bangalore, India}

[email protected]

2 _{IBM Research Lab, New Delhi, India}

[email protected]

3 _{Computer Science & Engg, SCIDSE, Arizona State University, USA}

{skumar34,fmorstat,huan.liu}@asu.edu

Abstract. The popularity of social media as a medium for sharing information

has made extracting information of interest a challenge. In this work we provide a system that can return posts published on social media covering various aspects of a concept being searched. We present a faceted model for navigating social media that provides a consistent, usable and domain-agnostic method for extracting in- formation from social media. We present a set of domain independent facets and empirically prove the feasibility of mapping social media content to the facets we chose. Next, we show how we can map these facets to social media sites, living documents that change periodically to topics that capture the semantics expressed in them. This mapping is used as a graph to compute the various facets of interest to us. We learn a profile of the content creator, enable content to be mapped to semantic concepts for easy navigation and detect similarity among sites to either suggest similar pages or determine pages that express different views.