Ahmed Elsayed
ABSTRACT
Following the NoSQL (Not Only SQL) movement, more research work and applications are looking towards graph databases for their dynamic schema and natural representation of complex data1. In order to access/ search graph data in a single source, a number of search methods have been proposed in the literature. These methods range from graph query languages, pattern queries, template/form based search, to keyword search. One key challenge of these methods is the expressiveness and ease of use trade-off for query formulation. Formulating queries becomes more challenging when querying multiple heterogeneous data sources and when users are unaware of the structure of the underlying data. This paper reviews various methods proposed in the literature to query graph modeled data in two different settings; namely, single source and multiple heterogeneous data sources. Furthermore, the paper proposes ConteSaG, a technique for transparently querying multiple heterogeneous data sources. ConteSaG employs graph database to represent data residing in local sources with no need to create complex global schema or even to upfront integrate all data in a central source. Moreover, ConteSaG provides a context-based keyword search over the graph representations. Context-based keyword search allows users to search multiple data sources by determining the context of search terms without the need to have complete knowledge about the structure of data in the local sources or writing queries in a specific query language.
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Index Terms
- ConteSaG: context-based keyword search over multiple heterogeneous graph-modeled data
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