Approximate querying for the Property Graph Language Cypher

Graph databases are well-suited to managing large, complex, dynamically evolving datasets. However, for data that is irregular and heterogeneous, it may be difficult to formulate queries that precisely capture a user's information seeking requirements. This points to the need for approximate query processing capabilities that can automatically make changes to a so as to aid in the incremental discovery of relevant information. In this paper we motivate and explore techniques for providing such capabilities for the Cypher query language. This is the first time that query approximation has been investigated in the context of the property graph data model, which is becoming increasingly prevalent in research and industry

Term-Specific Eigenvector-Centrality in Multi-Relation Networks

Fuzzy matching and ranking are two information retrieval techniques widely used in web search. Their application to structured data, however, remains an open problem. This article investigates how eigenvector-centrality can be used for approximate matching in multi-relation graphs, that is, graphs where connections of many different types may exist. Based on an extension of the PageRank matrix, eigenvectors representing the distribution of a term after propagating term weights between related data items are computed. The result is an index which takes the document structure into account and can be used with standard document retrieval techniques. As the scheme takes the shape of an index transformation, all necessary calculations are performed during index tim

A synopsis based approach for XML fast approximate querying

In the last few years, XML has spread in many application fields and today it is used as a format to exchange data on the web, to ensure inter-operability among applications. Due to this success, the W3C has proposed a new query language, XQuery [25], specifically designed to query XML data. XQuery is a well-defined but rather complex language [14]. In this work we propose a new approach to overcome the problem of the high computational costs required by aggregate queries over massive XML data collections. In traditional relational warehouses [11] a similar problem is solved by means of fast approximate queries, that use concise data statistics based on histograms or on other statistical techniques. Their most common application is for aggregate queries in modern decision support systems, where large volumes of data need to be queried, and quick and interactive responses from the DBMS are claimed, e.g., to analyze the data in the warehouse in order to get trend information to evaluate marketing strategies. In such applications, users are often more interested to obtain an approximate answer computed in a short time rather than an exact one obtained in some minutes or, at the worst, hours

Graph Summarization

The continuous and rapid growth of highly interconnected datasets, which are both voluminous and complex, calls for the development of adequate processing and analytical techniques. One method for condensing and simplifying such datasets is graph summarization. It denotes a series of application-specific algorithms designed to transform graphs into more compact representations while preserving structural patterns, query answers, or specific property distributions. As this problem is common to several areas studying graph topologies, different approaches, such as clustering, compression, sampling, or influence detection, have been proposed, primarily based on statistical and optimization methods. The focus of our chapter is to pinpoint the main graph summarization methods, but especially to focus on the most recent approaches and novel research trends on this topic, not yet covered by previous surveys.Comment: To appear in the Encyclopedia of Big Data Technologie