Understanding Student Errors in Graph Query Formulation

Practical and comprehensive formalisms for modelling contemporary graph query languages

One of the key differences between graph and relational databases is that on graphs we are much more interested in navigational queries. As a consequence, graph database systems are specifically engineered to answer these queries efficiently, and there is a wide body of work on query languages that can express complex navigational patterns.

Graph data management and matching similar graphs are very important for many applications including bioinformatics, computer vision, VLSI design, bug localization, road networks, social and communication networking. Many graph indexing and similarity matching techniques have already been proposed for managing and querying graph data. In similar graph matching, a user is returned with the database graphs whose distances with the query graph are below a threshold. In such query settings, a user may not receive certain database graphs that are very similar to the query graph if the initial query graph is inappropriate/imperfect for the expected answer set. To exemplify this, consider a drug designer who is looking for chemical compounds that could be the target of her hypothetical drug before realizing it. In response to her query, the traditional search system may return the structures from the database that are most similar to the query graph. However, she may get surprised if some of the expected targets are missing in the answer set. She may then seek assistance from the system by asking “Is there other query graph that can match my expected answer set?”. The system may then modify her initial query graph to include the missing answers in the new answer set. Here, we study this kind of problem of answering why-not questions in similar graph matching for graph databases.

Network analytics in the big graph database era: A research framework and agenda for business and innovation

Graph databases are becoming pervasive in several application scenarios such as the Semantic Web, social and biological networks, and geographical databases, to name a few. However, specifying a graph query is a cumbersome task for non-expert users because graph databases (i) are usually of large size hence difficult to visualize and (ii) do not carry proper metadata as there is no clear distinction between the instances and the schemas. We present GPS, a system for interactive path query specification on graph databases, which assists the user to specify path queries defined by regular expressions. The user is interactively asked to visualize small fragments of the graph and to label nodes of interest as positive or negative, depending on whether or not she would like the nodes as part of the query result. After each interaction, the system prunes the uninformative nodes i.e., those that do not add any information about the user's goal query. Thus, the system also guides the user to specify her goal query with a minimal number of interactions.

Graph theory, which is a branch of discrete mathematics, has emerged as a powerful tool in various domains, including database management. This abstract investigates the ways in which ideas and methods from graph theory which can be applied to database systems, offering a thorough synopsis of their benefits. Complex interactions within data can be well-modeled by using the basic concepts of graph theory, such as nodes, edges, and relationships. Because of its capacity to represent and query complex relationships, graph databases have become more and more popular in the field of database administration. Graph databases are well-suited for situations such as social networks, recommendation systems, and interconnected data domains because they are excellent at representing and traversing relationships, in contrast to standard relational databases, which are excellent at managing structured data. The abstract delves into the key graph-based data models, such as property graphs, RDF (Resource Description Framework), explaining how they facilitate the representation of diverse relationships. Furthermore, it explores the efficient storage and retrieval mechanisms that leverage graph traversal algorithms to extract valuable insights from interconnected datasets. The document highlights specific use cases where graph theory contributes to database management, including fraud detection, social network analysis, and recommendation systems. Additionally, it discusses the challenges associated with integrating graph databases into existing infrastructures and proposes solutions to address scalability and performance concerns. The abstract also touches upon the advancements in graph database query languages (Cypher) and SPARQL, showcasing their expressive power in querying complex relationships. The inclusion of graph-based indexing and optimization techniques demonstrates how database systems can efficiently handle queries involving large-scale graph data. As graph databases continue to evolve, this abstract concludes by outlining potential future directions in the intersection of graph theory and database management. It emphasizes the importance of ongoing research in developing scalable and efficient solutions for managing interconnected data, ultimately paving the way for more sophisticated and context-aware database systems relationships. Furthermore, it explores the efficient storage and retrieval mechanisms that leverage graph traversal algorithms to extract valuable insights from interconnected datasets.

An important step in data analysis is the exploration of data. For traditional relational databases one of the most powerful tools for performing such analysis is the relational database and the aggregates and rankings that they can compute: for instance, simple statistics such as the average number of links between two types of entities (relations) are easily computed using a query on a relational database and may already provide valuable information. However, for the exploration of graph data, relational databases may not be most practical and scalable. For instance, a statistic such as the shortest path between two given nodes cannot be computed by a relational database. Surprisingly, however, tools for querying graph and network databases are much less well developed than for relational data, and only recently an increasing number of studies are devoted to graph or network databases. Our position is that the development of such graph databases is important both to make basic graph mining easier and to prepare data for more complex types of analysis. An important component of such databases is the language that is used to enable aggregating queries, such as shortest path queries.In this paper, we propose an extension to a previously proposed query language. This extension allows for querying and analyzing databases by using aggregates and ranking. A notable feature of our language is that it also supports probabilistic graph queries by conceiving of such queries as aggregating queries. We demonstrate its value on a simple data analysis task.

Graph matching, as one of the most fundamental problems in graph database, has a wide range of applications. Due to the large scale of graph database and the hardness of graph matching, it is an attractive alternative to make use of the cloud to store massive data graphs and conduct the complex graph matching. To protect the privacy, data graphs are usually encrypted before being outsourced to the cloud. A few schemes have been proposed to support graph matching query over encrypted graphs. However, none of them can realize efficient subgraph extraction when the matched subgraph needs to be exactly located at the data graph. The graph user has to perform the complex subgraph isomorphism operation to extract the isomorphic subgraph from the matched data graph in state-of-the-art schemes. The time complexity of judging subgraph isomorphism is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(n!n^{2})$</tex-math></inline-formula> at most, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the number of vertices in the data graph. In order to solve this problem, we propose a privacy-preserving graph matching query scheme supporting quick subgraph extraction in this paper. In our design, two non-colluding cloud servers are adopted to accomplish the matching operation jointly. Neither of them can infer the plaintexts of graphs. The first cloud server can prune some impossible matched vertices from the data graph, and get a possible matched matrix to represent which vertices in the data graph might match with the vertices in the query graph. The second cloud server performs the related matrix operations based on its stored information and the information sent by the first cloud server. The two cloud servers jointly verify the correctness of the matched matrix. The graph user can directly and quickly extract the matrix of the subgraph isomorphic to the query graph from the data graph matrix based on the matched matrix, and further recover this subgraph. No subgraph isomorphism operation is involved in the procedure of subgraph extraction for the graph user. The time complexity of subgraph extraction is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(m^{2})$</tex-math></inline-formula> in our scheme, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> is the number of vertices in the query graph. The extensive experiments with real-world database demonstrate the efficiency of the proposed privacy-preserving graph matching scheme.

Application domains such as bioinformatics and web technology represent complex objects as graphs where nodes represent basic objects (i.e. atoms, web pages etc.) and edges model relations among them. In biochemical databases proteins are naturally represented as labeled graphs: the nodes are atoms and the edges are chemical links. In computer vision, graphs can be used to represent images at different levels of abstraction. In a low-level representation (Pailloncy, Deruyver, &amp; Jolion, 1999), the nodes of a graph correspond to pixels and the edges to spatial relations between pixels. At higher levels of description (Hong &amp; Huang, 2001), nodes are image regions and edges are spatial relations among them. In a Web graph (Deutsch, Fernandez, Florescu, Levy, &amp; Suciu, 1999) nodes are web pages and edges are links between them. In all these domains, substructure queries that search for all exact or approximate occurrences of a given query graph in the graphs of the database can be useful. Research efforts in graph searching have taken three directions: the first is to study matching algorithms for particular graph structures (planar graphs, bounded valence graphs and association graphs); the second is to use elaborate tricks to reduce the number of generated matching maps (Cordella, Foggia, Sansone, &amp; Vento, 2004); and the third is, since graph searching problem is NP-complete, to provide polynomial approximate algorithms. In the context of querying in a database of graphs many of the existing methods are designed for specific applications. For example, several querying methods for semi-structured databases have been proposed. In addition, commercial products and academic projects (Kelley 2002) for subgraph searching in biochemical databases are available. These two examples have a different underlying data model (a web-page database is a large graph whereas a biochemical database is a collection of graphs). In these two applications regular path expressions and indexing methods are used during query time to respectively locate substructures in the database and to avoid unnecessary traversals of the database. In general graph databases, there are some searching methods where the data graph and the query graph must have the same size. Other methods allow the query to be considerably smaller than the database graphs. A common idea in the above algorithms is to index the subgraph matches in the database and organize them in suitable data structures.

This work presents Fragment Graph DataBase (FGDB), a graph database of ligand fragments extracted and generated from the protein entries available in the Protein Data Bank (PDB). FGDB is meant to support and elicit campaigns of fragment-based drug design, by enabling users to query it in order to construct ad hoc, target-specific libraries. In this regard, the database features more than 17 000 fragments, typically small, highly soluble and chemically stable molecules expressed via their canonical Simplified Molecular Input Line Entry System (SMILES) representation. For these fragments, the database provides information related to their contact frequencies with the amino acids, the ligands they are contained in and the proteins the latter bind to. The graph database can be queried via standard web forms and textual searches by a number of identifiers (SMILES, ligand and protein PDB ids) as well as via graphical queries that can be performed against the graph itself, providing users with an intuitive and effective view upon the underlying biological entities. Further search mechanisms via advanced conjunctive/disjunctive/negated textual queries are also possible, in order to allow scientists to look for specific relationships and export their results for further studies. This work also presents two sample use cases where maternal embryonic leucine zipper kinase and mesotrypsin are used as a target, being proteins of high biomedical relevance for the development of cancer therapies.Database URL: http://biochimica3.bio.uniroma3.it/fragments-web/

The property graph model has recently gained significant popularity, combining great expressiveness with powerful declarative graph query languages. However, in order to take advantage of these features, data must be loaded into a specialised graph database. Additionally, property graphs are often schema-free, complicating efficient query execution. In this paper we present Cytosm, a middleware application which enables the execution of property graph queries, on non-graph databases, without data migration. Cytosm relies on gTop, a schema containing an abstract property graph topology, and its mapping to specific database backends. Cytosm uses gTop to efficiently execute OpenCypher queries, exploiting schema information to optimise the query plan, and mapping query concepts to the relational backend. Our experiments show that Cytosm achieves competitive query execution times on relational backends, when compared to leading graph databases.

The software architecture is typically defined as the fundamental organization of the system embodied in its components, their relationships to one another and to the system's environment. It also encompases principles governing the system's design and evolution. In order to manage the architecture of a large software system the architect needs a holistic model that supports continuous integration and verification for all system artifacts. In earlier papers we proposed a unified graph-based approach to the problem of managing knowledge about the architecture of a software system. In this paper we demonstrate that this approach facilitates convenient and efficient project measurement. First, we show how existing software metrics can be translated into our model in a way that is independent of the programming language. Second, we introduce new metrics that cross the programming language boundaries and are easily implementable using our approach. We conclude by demonstrating how the new model can be implemented using existing tools. In particular, graph databases are a convenient implementation of an architectural repository. Graph query languages and graph algorithms are an effective way to define metrics and specialized graph views.

Graph databases in many applications - semantic web, transport or biological networks among others - are not only large, but also frequently modified. Evaluating graph queries in this dynamic context is a challenging task, as those queries often combine first-order and navigational features. Motivated by recent results on maintaining dynamic reachability, we study the dynamic evaluation of traditional query languages for graphs in the descriptive complexity framework. Our focus is on maintaining regular path queries, and extensions thereof, by first-order formulas. In particular we are interested in path queries defined by non-regular languages and in extended conjunctive regular path queries (which allow to compare labels of paths based on word relations). Further we study the closely related problems of maintaining distances in graphs and reachability in product graphs. In this preliminary study we obtain upper bounds for those problems in restricted settings, such as undirected and acyclic graphs, or under insertions only, and negative results regarding quantifier-free update formulas. In addition we point out interesting directions for further research.

Several query languages have been proposed in which users graphically manipulate Entity-Relationship (ER) diagrams to formulate queries. In this paper we present a method of implementing an ER query and update language as an interface for a relational Database Management System. The approach is based on interactive manipulation of ER database schemas and generation of SQL expressions for an underlying relational database. The graphical query and update formulation process is formally defined. We assume that the information reflecting the current state of the query and update formulation process is maintained in a formal model. The model described in other publications is significantly modified and extended. The information included in the extended model allows an ER diagram corresponding to the current state of the query to be displayed on the terminal screen. It is also used to generate an equivalent SQL expression for a relational database. The integrity constraints for the formal model are specified.

To meet the challenge of analyzing rapidly growing graph and network data created by modern applications, a large number of specialized graph databases have emerged, such as Neo4j, JanusGraph, and Sqlg. At the same time, RDBMSs and SQL continue to support mission-critical business analytics. However, real-life analytical applications seldom contain only one type of analytics. They are often made of heterogeneous workloads, including SQL, machine learning, graph, and other analytics. In particular, SQL and graph analytics are usually accompanied together in one analytical workload. This means that graph and SQL analytics need to be synergistic with each other. Unfortunately, most existing graph databases are standalone and cannot easily integrate with relational databases. In addition, as a matter of fact, many graph data (data about relationships between objects or people) are already prevalent in relational databases, although they are not explicitly stored as graphs. Performing graph analytics on these relational graph data today requires exporting large amount of data to the specialized graph databases. A natural question arises: can SQL and graph analytics be performed synergistically in a same system? In this demo, we present such a working system called IBM Db2 Graph. Db2 Graph is an in-DBMS graph query approach. It is implemented as a layer inside an experimental IBM Db2™, and thus can support synergistic graph and SQL analytics efficiently. Db2 Graph employs a graph overlay approach to expose a graph view of the relational data. This approach flexibly retrofits graph queries to existing graph data stored in relational tables. We use an example scenario on health insurance claim analysis to demonstrate how Db2 Graph is used to support synergistic graph and SQL analytics inside Db2.