Scalable Hybrid Keyword Search on Distributed Database Jungkee Kim Florida State University Community Grids Laboratory, Indiana University Workshop on Autonomic Distributed Data and Storage Systems Management (ADSM 2005)

Motivation Internet Where is the Information?

Outline Two Typical Search Paradigms Problems of Current Search Approaches Local Hybrid Keyword Search Hybrid Search on Distributed Databases

Two Typical Search Paradigms Searching over structured data Relational Databases Searching over unstructured data Information Retrieval Internet Environment Semistructured Data – XML Keyword Search in DB Web Search Engines – Technologies from Information Retrieval Hybrid Keyword Search ?

Current Approaches – Keyword-only Search Web Search Engines  Web crawlers visit Web pages and collect the keyword based text indexes.  Fast information retrieval Keyword Search in databases  Web integration on legacy DBMS  Dynamic Web publication through embedded DB  Easy to use without knowledge of DB schema

Problems of Current Approaches – Keyword-based Web Search Engines  Can not collect every connected resource  Query results are often unrelated Keyword Search in Databases  Losing the inherent meaning of the schema  Query results are not based on semantic schema

Current Approaches – Semantic Semantic Web  Multiple relation links with directed labeled graphs and machines can understand the relationship between different resources  Describes metadata about resources  To represent the relations of the objects on the Web; the object terms defined under a specific description – an Ontology

Problems of Current Approaches – Semantic Web Ontology design is sophisticated Lack of unified definition ** Limited adoption

Our Approach Hybrid search mechanisms – Semantic metadata + Keyword search Semantic Solution Semantic Web might be better than Hybrid search Hybrid search must be better than Web search engines Simplicity Hybrid search is simpler than Semantic Web

Hybrid Keyword Search Service A search service fetches target information data against a search query. Unstructured data A file containing data – MS Word, PDF, PS documents Metadata: Structured or semistructured data – XML We utilized an XML-enabled relational DBMS and a native XML DB along with a text search library (Apache Xindice + Jakarta Lucene) to address the search against metadata and text.

How to Combine? (1) Two entity sets and a relationship in relational DBMS We can obtain the hybrid search result using a nested subquery

How to Combine? (2) A hash table is used for joining search results in non- DBMS based system (Apache Xindice + Lucene)

Local Query Processing – XML (1) XML-enabled RDB  DBLP XML record (1,000 – 10,000)  Non indexed matches except year match bound by the number of matches.  Combined query time depends on # of year query results Average XML Query Time

Local Query Processing – XML (2) Apache Xindice  DBLP XML record (1,000 – 10,000)  Indexed approximate matches for text elements in XML instances as bad as non- indexed queries  Exact matches bound by the number of matches. Average XML Query Time

Local Query Processing – Hybrid (1) Hybrid search query performance measurement  XML-enabled RDB  For 100,000 XML instances and 100,000 text documents  Small result set: 4 XML and a keyword matches  Large result set: 7,752 XML and 41,889 documents (3,227) MetadataAuthorYear (Nested subquery) Year (Hash table) Few Keywords 0.04 Sec Sec.5.70 Sec. Many Keywords 0.48 Sec. Half hour6.96 Sec.

Local Query Processing – Hybrid (2) Hybrid search query performance measurement  Apache Xindice + Jakarta Lucene  For 10,000 XML instances and 10,000 text documents  Small result set: 2 XML and a keyword matches  Large result set: 192 XML and 4,562 documents (41)

Discussion – Local Hybrid Search XML-enabled RDB provides proper response except some extreme query loads.  Inefficient query plan and query optimization in an old version – better performance in a newer version A native XML DB (Apache Xindice) had very limited scalability. (No accurate query result over 16,000 XML instances) We will generalize hybrid search to a distributed environment.

Hybrid Search on Distributed Databases Data Independence: logically and physically independent; the same schema – no change, data encapsulation in each machine Network Transparency: depends on MOM or P2P framework No replication – restricted to a computer cluster Fragment: full partition; horizontal fragmentation The query result for the distributed databases is the collection of query results from individual database queries.

Scalable Hybrid Search Architecture on DDBS Search Service Message Broker Client Search Service Search Service Subscriber for a query topic Publisher for a temporary topic Publisher for a query topic Subscriber for a temporary topic Query Message Query Message Result Message Result Message Client

Cooperating Broker Network Distributed Databases based on NaradaBrokering Network

Query Processing – DDBS (1) 100,000 XML and 100,000 Documents in 8 machines – 12,500 each Few keyword match (1-3) on 1 machine only RDB – 0.04 Sec. for few keyword match Avg. response time for an author exact match query over 8 search services

Query Processing – DDBS (2) 100,000 XML and 100,000 Documents in 8 machines – 12,500 each RDB – half hour or 6.96 Sec. (Hash table) Avg. response time for a year match query over 8 search services

Coupling vs. Scalability From ICDE 2002 Tutorial

Query Propagate and Results back on a P2P Network

Peer group architecture of the P2P Search

Conclusion We addressed the semantic loss of keyword-only search while remaining a simpler solution than the Semantic Web Our architecture contributed a performance improvement for some queries Extension of the scalability of Xindice XML query limited to a small size on a single machine