When a graph database is implemented on top of a relational database, queries in the graph query language are translated into relational SQL queries. Graph pattern queries are an important feature of a graph query language. Translating graph pattern queries into single SQL statements results in very poor query performance. By taking into account the pattern query structure and generating multiple SQL statements, pattern query performance can be dramatically improved. The performance problems encountered with the single SQL statements generated for pattern queries reflects a problem in the SQL query planner and optimizer. Addressing this problem would allow relational databases to better support semantic graph databases. Relational database systems that provide good support for graph databases may also be more flexible platforms for data warehouses. es_194 table is the vertex table that contains the attribute values for the JournalIssue vertices.
Large graph analysis has become increasingly important and is widely used in many applications such as web mining, social network analysis, biology, and information retrieval. The usually high computational complexity of the commonly-used graph algorithms and large volume of data frequently encountered in these applications, however, make scalable graph analysis a non-trivial task.Recently, more and more of these graph algorithms are implemented as dataflow applications, where many tasks perform assigned operations in parallel independent of other tasks. These applications are run on large-scale computing platforms to combine the advantages of the data parallelism enabled by dataflow model and the high computing power and large storage capacity offered by increasingly affordable high-end computers. In this paper, we evaluate the potentials of many-tasks concept in a form of dataflow system for large graph analysis applications by studying the performance of complicated graph algorithms on an actual dataflow machine. We have found that a dataflow system can achieve orders of magnitude performance improvement over state-of-art database systems and serve as a viable scalable graph analysis engine.
Semantic graphs can be used to organize large amounts of information from a number of sources into one unified structure. A semantic query language provides a foundation for extracting information from the semantic graph. The graph query language described here provides a simple, powerful method for querying semantic graphs. filter ("MyBaseGraph", vertex { person where name = "William S. Burroughs", person where name = "Andy Warhol" } edge {none}), vertex { city } edge { lives_in });
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