Abstract. Hierarchical structures are a way to organize and enrich semantically the available information on the Web. Popular examples of such structures are the product catalogs of e-market stores, which provide data (i.e. products) organized in thematic
Figure 20: Resulting TST after the difference operation in case 1.
Having as a result only the lenses without the appropriate camera bodies, requires a projection operation which keeps only the attribute lmodel and the path /photo/35mm systems/lenses (see Figure 21).
X now needs to build a new catalog with her own, old one, plus all integrated photo systems, having ‘Canon’ camera bodies from Adorama’s catalog and lenses from B&H catalog, to compare their prices. To create complex resource items (i.e. camera bodies and lenses as an integrated package), the user applies again the cartesian product operator on SLR cameras and lenses to construct a new TSR1.
Figure 21: Final TSR for case 1.
From all records of the resource item node of TSR1, the user keeps only those records (a) referring to ‘Canon’ camera bodies and (b) having lenses that fit these bodies. From all the attributes, the user keeps cbrand, cmodel, cprice, lmodel, lprice. TSR TSR1 is identical to the one in Figure 19, except that there are more attributes in its resource item.
Abstract. Hierarchical structures are a way to organize and enrich semantically the available information on the Web. Popular examples of such structures are the product catalogs of e-market stores, which provide data (i.e. products) organized in thematic
The union operator on TSR1 and SLR Systems results in the TSR TSR2 (see Figure 22). TSR2 has two OR components, showing all integrated photo systems from all three catalogs. It includes systems
with ‘Canon’ bodies from Adorama and lenses from B&H, as well as systems from X.
Figure 22: TSR TSR2 in case 2.
Searching for paths that lead to lenses including nodes photo and lenses requires a selection operator (see Figure 23). A final projection will produce a TSR schema only for lenses (see Figures 24, 25).
Figure 23: Applying select operator.
Figure 24: Applying project operator.
Figure 25: Final TSR for case 2.
6. Conclusions & Further Work
In this paper we described PatMan, a prototype visual database system to manage hierarchical catalogs. Such catalogs are represented with catalog schemas. Several catalog schemas can be combined, creating
Abstract. Hierarchical structures are a way to organize and enrich semantically the available information on the Web. Popular examples of such structures are the product catalogs of e-market stores, which provide data (i.e. products) organized in thematic
tree-structured relations (TSRs). TSRs are modeling structures that emphasize the role of paths as knowledge artifacts. They capture the different ways of accessing data in a set of catalog schemas, and maintain alternative path-like pattern versions and complex patterns.
The PatMan system manipulates navigational path patterns and data from hierarchical catalogs in a uniform way, providing:
1. mechanisms to import structures from hierarchical catalogs coming in various forms (e.g. RDFs
representation),
2. pictorial query-by-example capabilities based on the PatManQL, a query language with the
operators select, project, cartesian product, union, intersection and difference to manipulate TSRs from hierarchical catalogs.
3. visualization capabilities to explore navigational paths for hierarchical catalogs as well as the
raw data organized in these catalogs.
We plan to extend the work presented in this paper along several directions. First, we will further explore the role of paths as knowledge artifacts in hierarchical catalogs, searching for useful comparison operators for paths. Also, we will introduce additional operators for path management. For example, one can identify the need for a join operator based on the select and cartesian product operators.
7. References
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2. P. Bouros, T. Dalamagas, T. Sellis, M. Terrovitis: PatManQL, A language to Manipulate Patterns and Data in
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3. A. Chaudhri, A. Rashid, R. Zicari: XML Data Management: Native XML and XML-Enabled Database
Systems, Addison Wesley, 2003.
4. V. Christophides, S. Cluet, J. Simeon: On wrapping query languages and efficient XML integration, Proc. of
the ACM SIGMOD Conf., p141-152, 2000.
5. J. Han, Y. Fu, W. Wang, K. Koperski, O. Zaiane: DMQL: A Data Mining Query Language for Relational
Databases, Proc. of the SIGMOD'96 DKMD Workshop, Mondreal, Canada, 1996.
6. T. Imielinski, H. Mannila: A Database Perspective on Knowledge Discovery, Commun. ACM, 39(11), 1996. 7. H. V. Jagadish, L. V. S. Lakshmanan, D. Srivastava, K. Thompson: TAX: A Tree Algebra for XML, Proc. of
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