Extending SQL for computing shortest paths

Reachability and shortest paths are among two of the most common queries realized on graphs. While graph frameworks and property graph databases provide an extensive and convenient built-in support for these operations, it is still both clunky and inefficient to perform on standard SQL DBMSs. In this paper, we present an extension to the standard SQL language to compute both reachability predicates and many-to-many shortest path queries. We first describe a methodology to represent a directed graph starting from virtual table expressions. Second, we introduce a new type of operator to compute shortest

Dynamic Complexity of Planar 3-connected Graph Isomorphism

Dynamic Complexity (as introduced by Patnaik and Immerman) tries to express how hard it is to update the solution to a problem when the input is changed slightly. It considers the changes required to some stored data structure (possibly a massive database) as small quantities of data (or a tuple) are inserted or deleted from the database (or a structure over some vocabulary). The main difference from previous notions of dynamic complexity is that instead of treating the update quantitatively by finding the the time/space trade-offs, it tries to consider the update qualitatively, by finding the complexity class in which the update can be expressed (or made). In this setting, DynFO, or Dynamic First-Order, is one of the smallest and the most natural complexity class (since SQL queries can be expressed in First-Order Logic), and contains those problems whose solutions (or the stored data structure from which the solution can be found) can be updated in First-Order Logic when the data structure undergoes small changes. Etessami considered the problem of isomorphism in the dynamic setting, and showed that Tree Isomorphism can be decided in DynFO. In this work, we show that isomorphism of Planar 3-connected graphs can be decided in DynFO+ (which is DynFO with some polynomial precomputation). We maintain a canonical description of 3-connected Planar graphs by maintaining a database which is accessed and modified by First-Order queries when edges are added to or deleted from the graph. We specifically exploit the ideas of Breadth-First Search and Canonical Breadth-First Search to prove the results. We also introduce a novel method for canonizing a 3-connected planar graph in First-Order Logic from Canonical Breadth-First Search Trees

Reasoning & Querying – State of the Art

Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF

Plug-and-Play SQL

We present an efficient model to retrieve data from a database by implementing plug-and-play queries using the query guards. The model is efficient in the sense that it saves time when writing a query and promotes query portability and reuse. A plug-and-play query is a freestanding query that can couple to any data socket and self determine whether it can be evaluated reliably on the data. We use hierarchies to improve SQL querying in a way that eliminates the need to write a view to construct virtual tables or a set of tables to run a query. The hierarchy is a declarative specification of the desired shape of data rather than a description of how the data is organized. The advantage is that the common use of logical or semantic pointers in the SQL queries is eliminated and a natural way to group data for aggregation is provided. The plug-and-play queries have several advantages, they are portable and can be used to evaluate any data source

Detection and Prevention of Unknown Vulnerabilities on Enterprise IP Networks

Computer networks have long become the backbone of Enterprise Information System. The substantial share of the security problems are still encountered in Enterprise Network. Cyber espionage can effect Ethical, Military, Political and Economic interest anywhere. To provide secure computer networks, it is necessary to measure the relative effectiveness of security solution in the network. A network security metric enable a direct measurement and comparison of the amounts of security provided by different security solutions .In this paper we propose a novel security metric Zero Day Vulnerability Prevention Framework consists of bunches of algorithms. The above framework detects and prevents unknown vulnerabilities in Enterprise IP networks. It also protects the behavior of the sessions performed by the user from the huge range of attacks. It helps in monitoring database requests and prevents the attacks. The proposed framework also implements worm and virus detection to evaluate malware from the data. The system also presents scoring to the vulnerabilities and finally it performs security analysis with the help of Topological Vulnerability Analysis (TVA) tool. DOI: 10.17762/ijritcc2321-8169.15028

An introduction to Graph Data Management

A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graph-oriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them

Graph Pattern Matching in GQL and SQL/PGQ

As graph databases become widespread, JTC1 -- the committee in joint charge of information technology standards for the International Organization for Standardization (ISO), and International Electrotechnical Commission (IEC) -- has approved a project to create GQL, a standard property graph query language. This complements a project to extend SQL with a new part, SQL/PGQ, which specifies how to define graph views over an SQL tabular schema, and to run read-only queries against them. Both projects have been assigned to the ISO/IEC JTC1 SC32 working group for Database Languages, WG3, which continues to maintain and enhance SQL as a whole. This common responsibility helps enforce a policy that the identical core of both PGQ and GQL is a graph pattern matching sub-language, here termed GPML. The WG3 design process is also analyzed by an academic working group, part of the Linked Data Benchmark Council (LDBC), whose task is to produce a formal semantics of these graph data languages, which complements their standard specifications. This paper, written by members of WG3 and LDBC, presents the key elements of the GPML of SQL/PGQ and GQL in advance of the publication of these new standards

A Data Science Course for Undergraduates: Thinking with Data

Data science is an emerging interdisciplinary field that combines elements of mathematics, statistics, computer science, and knowledge in a particular application domain for the purpose of extracting meaningful information from the increasingly sophisticated array of data available in many settings. These data tend to be non-traditional, in the sense that they are often live, large, complex, and/or messy. A first course in statistics at the undergraduate level typically introduces students with a variety of techniques to analyze small, neat, and clean data sets. However, whether they pursue more formal training in statistics or not, many of these students will end up working with data that is considerably more complex, and will need facility with statistical computing techniques. More importantly, these students require a framework for thinking structurally about data. We describe an undergraduate course in a liberal arts environment that provides students with the tools necessary to apply data science. The course emphasizes modern, practical, and useful skills that cover the full data analysis spectrum, from asking an interesting question to acquiring, managing, manipulating, processing, querying, analyzing, and visualizing data, as well communicating findings in written, graphical, and oral forms.Comment: 21 pages total including supplementary material