Functional Programming Languages and Computer Architecture

Fuzziness in database management systems

Welcome to SIGMOD 2008! We think you will find both the conference and the setting to be invigorating. The natural timeless beauty of British Columbia will provide a fitting counterpoint to the dynamism of our field in which large scale, high performance, and ever more intelligent database systems are being conceived and deployed. This dynamism is reflected in our (extreme) keynote presentations, tutorials, research papers, demonstrations, industrial papers, and product presentations. The only unfortunate side of our program is that the five parallel session structure may prevent you from hearing every talk in which you are interested.

The conference statistics give an indication of how SIGMOD's selectivity. Out of 435 submitted research papers, we accepted 78; out of 40 submitted industrial papers, we accepted 15; out of 94 demo submissions, we accepted 30; and out of 15 tutorial submissions, we accepted 5. Reviewing is an imperfect art, so we may have rejected some papers that we should have accepted, but we hope the written reviews have helped authors improve their papers for future submissions.

The main methodological innovation in SIGMOD this year has been the repeatability option. Papers submitting experiments were invited to submit code and data to enable the pioneering members of the repeatability committee to verify that the experiments worked as advertised. Any paper satisfying the repeatability criteria will include the sentence "The results in this paper were verified by the SIGMOD repeatability committee." The goal is to count our field among the repeatable sciences and to pave the way for the archiving of code and data. The response to this initiative has been overwhelmingly positive and we look forward to a greater participation by all members of the community in the future.

Proceedings of the 2008 ACM SIGMOD international conference on Management of data

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On the recursive programming techniques

Graph processing has become an important part of multiple areas of computer science, such as machine learning, computational sciences, medical applications, social network analysis, and many others. Numerous graphs such as web or social networks may contain up to trillions of edges. Often, these graphs are also dynamic (their structure changes over time) and have domain-specific rich data associated with vertices and edges. Graph database systems such as Neo4j enable storing, processing, and analyzing such large, evolving, and rich datasets. Due to the sheer size of such datasets, combined with the irregular nature of graph processing, these systems face unique design challenges. To facilitate the understanding of this emerging domain, we present the first survey and taxonomy of graph database systems. We focus on identifying and analyzing fundamental categories of these systems (e.g., triple stores, tuple stores, native graph database systems, or object-oriented systems), the associated graph models (e.g., RDF or Labeled Property Graph), data organization techniques (e.g., storing graph data in indexing structures or dividing data into records), and different aspects of data distribution and query execution (e.g., support for sharding and ACID). 45 graph database systems are presented and compared, including Neo4j, OrientDB, or Virtuoso. We outline graph database queries and relationships with associated domains (NoSQL stores, graph streaming, and dynamic graph algorithms). Finally, we describe research and engineering challenges to outline the future of graph databases.

Demystifying Graph Databases: Analysis and Taxonomy of Data Organization, System Designs, and Graph Queries.

Graph databases are taking more and more importance, especially for social networking. For instance, organizations can implement graph databases to represent and query data such as Person1 is CEO of Organization1. NoSQL graph databases (e.g., Neo4j) have been designed to deal with such data. However, managing history is not yet possible in an easy manner while being critical in many applications. Tracking changes is indeed one of the main functionalities in databases (especially Relational BD) and should not be forsaken in NoSQL graph DB. For instance, queries like “list all the people who have been CEO of Organization1” or “list all the functions People1 has been taken in his career” are important. In this paper, we thus propose a novel representation of historical graph data and tools to implement it as a plug-in of existing NoSQL graph systems.

/pdf/representing-history-in-graph-oriented-nosql-databases-a-17j4ur7l6h.pdf

Representing history in graph-oriented NoSQL databases: A versioning system

When querying databases, users often wish to express vague concepts, as for instance asking for the cheap hotels. This has been extensively studied in the case of relational databases. In this paper, we propose to study how such useful techniques can be adapted to NoSQL graph databases where the role of fuzziness is crucial. Such databases are indeed among the fastest-growing models for dealing with big data, especially when dealing with network data (e.g., social networks). We consider the Cypher declarative query language proposed for Neo4j which is the current leader on this market, and we present how to express fuzzy queries.

https://hal-lirmm.ccsd.cnrs.fr/lirmm-01381080/document

Fuzzy Queries over NoSQL Graph Databases: Perspectives for Extending the Cypher Language

OLAP is a leading technology for analysing data and decision making. It helps the users to discover relevant information from large databases. Graph OLAP has been studied for several years in the OLAP framework. In existing work, the authors study how to import graph data into OLAP cube models but no work has explored yet the feasability to exploit graph structures to store analytical data. As graph databases are more and more used through NoSQL implementations (e.g., social and biological networks), in this paper we aim at providing an original model for managing cubes into NoSQL graphs. We show how cubes can be represented in graphs and how these structures can then be used for graph OLAP queries to support decision making.

/pdf/nosql-graph-based-olap-analysis-32k1qhva2r.pdf

NoSQL Graph-based OLAP Analysis

Linguistic summaries have been studied for many years and allow to sum up large volumes of data in a very intuitive manner. They have been studied over several types of data. However, few works have been led on graph databases. Graph databases are becoming popular tools and have recently gained significant recognition with the emergence of the so-called NoSQL graph databases. These databases allow users to handle huge volumes of data (e.g., scientific data, social networks). There are several ways to consider graph summaries. In this paper, we detail the specificities of NoSQL graph databases and we discuss how to summarize them by introducing several types of linguistic summaries, namely structure summaries, data structure summaries and fuzzy summaries. We present extraction methods that have been tested over synthetic and real database experimentations.

/pdf/extracting-fuzzy-summaries-from-nosql-graph-databases-362i24er8o.pdf

Extracting Fuzzy Summaries from NoSQL Graph Databases

https://research-information.bris.ac.uk/files/147714288/Trevor_Martin_Handling_Scalable_Approximate_Queries_over_NoSQL.pdf

Arnaud Castelltort

Papers

Representing history in graph-oriented NoSQL databases: A versioning system

Fuzzy Queries over NoSQL Graph Databases: Perspectives for Extending the Cypher Language

NoSQL Graph-based OLAP Analysis

Extracting Fuzzy Summaries from NoSQL Graph Databases

Handling scalable approximate queries over NoSQL graph databases:: Cypherf and the Fuzzy4S framework