99,483 research outputs found
A Comparative Analysis of Graph Vs Relational Database For Instructional Module Development System
abstract: In today's data-driven world, every datum is connected to a large amount of data. Relational databases have been proving itself a pioneer in the field of data storage and manipulation since 1970s. But more recently they have been challenged by NoSQL graph databases in handling data models which have an inherent graphical representation. Graph databases with the ability to store physical relationships between two nodes and native graph processing technique have been doing exceptionally well in graph data storage and management for applications like recommendation engines, biological modeling, network modeling, social media applications, etc.
Instructional Module Development System (IMODS) is a web-based software system that guides STEM instructors through the complex task of curriculum design, ensures tight alignment between various components of a course (i.e., learning objectives, content, assessments), and provides relevant information about research-based pedagogical and assessment strategies. The data model of IMODS is highly connected and has an inherent graphical representation between all its entities with numerous relationships between them. This thesis focuses on developing an algorithm to determine completeness of course design developed using IMODS. As part of this research objective, the study also analyzes the data model for best fit database to run these algorithms. As part of this thesis, two separate applications abstracting the data model of IMODS have been developed - one with Neo4j (graph database) and another with PostgreSQL (relational database). The research objectives of the thesis are as follows: (i) evaluate the performance of Neo4j and PostgreSQL in handling complex queries that will be fired throughout the life cycle of the course design process; (ii) devise an algorithm to determine the completeness of a course design developed using IMODS. This thesis presents the process of creating data model for PostgreSQL and converting it into a graph data model to be abstracted by Neo4j, creating SQL and CYPHER scripts for undertaking experiments on both platforms, testing and elaborate analysis of the results and evaluation of the databases in the context of IMODS.Dissertation/ThesisMasters Thesis Computer Science 201
Extreme Acceleration of Graph Neural Network-based Prediction Models for Quantum Chemistry
Molecular property calculations are the bedrock of chemical physics.
High-fidelity \textit{ab initio} modeling techniques for computing the
molecular properties can be prohibitively expensive, and motivate the
development of machine-learning models that make the same predictions more
efficiently. Training graph neural networks over large molecular databases
introduces unique computational challenges such as the need to process millions
of small graphs with variable size and support communication patterns that are
distinct from learning over large graphs such as social networks. This paper
demonstrates a novel hardware-software co-design approach to scale up the
training of graph neural networks for molecular property prediction. We
introduce an algorithm to coalesce the batches of molecular graphs into fixed
size packs to eliminate redundant computation and memory associated with
alternative padding techniques and improve throughput via minimizing
communication. We demonstrate the effectiveness of our co-design approach by
providing an implementation of a well-established molecular property prediction
model on the Graphcore Intelligence Processing Units (IPU). We evaluate the
training performance on multiple molecular graph databases with varying degrees
of graph counts, sizes and sparsity. We demonstrate that such a co-design
approach can reduce the training time of such molecular property prediction
models from days to less than two hours, opening new possibilities for
AI-driven scientific discovery
Feature-Based Classification of Bidirectional Transformation Approaches
International audienceBidirectional model transformation is a key technology in model-driven engineering (MDE), when two models that can change over time have to be kept constantly consistent with each other. While several model transformation tools include at least a partial support to bidirectionality, it is not clear how these bidirectional capabilities relate to each other and to similar classical problems in computer science, from the view update problem in databases to bidirectional graph transformations. This paper tries to clarify and visualize the space of design choices for bidirectional transformations from an MDE point of view, in the form of a feature model. The selected list of existing approaches are characterized by mapping them to the feature model. Then, the feature model is used to highlight some unexplored research lines in bidirectional transformations
The Train Benchmark: cross-technology performance evaluation of continuous model queries
In model-driven development of safety-critical
systems (like automotive, avionics or railways), well-
formedness of models is repeatedly validated in order to
detect design flaws as early as possible. In many indus-
trial tools, validation rules are still often implemented by
a large amount of imperative model traversal code which
makes those rule implementations complicated and hard
to maintain. Additionally, as models are rapidly increas-
ing in size and complexity, efficient execution of validation rules is challenging for the currently available tools.
Checking well-formedness constraints can be captured by
declarative queries over graph models, while model update
operations can be specified as model transformations. This
paper presents a benchmark for systematically assessing the
scalability of validating and revalidating well-formedness
constraints over large graph models. The benchmark defines
well-formedness validation scenarios in the railway domain:
a metamodel, an instance model generator and a set of well-
formedness constraints captured by queries, fault injection
and repair operations (imitating the work of systems engi-
neers by model transformations). The benchmark focuses
on the performance of query evaluation, i.e. its execution
time and memory consumption, with a particular empha-
sis on reevaluation. We demonstrate that the benchmark
can be adopted to various technologies and query engines,
including modeling tools; relational, graph and semantic
databases. The Train Benchmark is available as an open-
source project with continuous builds from
https://github.
com/FTSRG/trainbenchmark
Using Ontologies for the Design of Data Warehouses
Obtaining an implementation of a data warehouse is a complex task that forces
designers to acquire wide knowledge of the domain, thus requiring a high level
of expertise and becoming it a prone-to-fail task. Based on our experience, we
have detected a set of situations we have faced up with in real-world projects
in which we believe that the use of ontologies will improve several aspects of
the design of data warehouses. The aim of this article is to describe several
shortcomings of current data warehouse design approaches and discuss the
benefit of using ontologies to overcome them. This work is a starting point for
discussing the convenience of using ontologies in data warehouse design.Comment: 15 pages, 2 figure
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Learning from AI : new trends in database technology
Recently some researchers in the areas of database data modelling and knowledge representations in artificial intelligence have recognized that they share many common goals. In this survey paper we show the relationship between database and artificial intelligence research. We show that there has been a tendency for data models to incorporate more modelling techniques developed for knowledge representations in artificial intelligence as the desire to incorporate more application oriented semantics, user friendliness, and flexibility has increased. Increasing the semantics of the representation is the key to capturing the "reality" of the database environment, increasing user friendliness, and facilitating the support of multiple, possibly conflicting, user views of the information contained in a database
gMark: Schema-Driven Generation of Graphs and Queries
Massive graph data sets are pervasive in contemporary application domains.
Hence, graph database systems are becoming increasingly important. In the
experimental study of these systems, it is vital that the research community
has shared solutions for the generation of database instances and query
workloads having predictable and controllable properties. In this paper, we
present the design and engineering principles of gMark, a domain- and query
language-independent graph instance and query workload generator. A core
contribution of gMark is its ability to target and control the diversity of
properties of both the generated instances and the generated workloads coupled
to these instances. Further novelties include support for regular path queries,
a fundamental graph query paradigm, and schema-driven selectivity estimation of
queries, a key feature in controlling workload chokepoints. We illustrate the
flexibility and practical usability of gMark by showcasing the framework's
capabilities in generating high quality graphs and workloads, and its ability
to encode user-defined schemas across a variety of application domains.Comment: Accepted in November 2016. URL:
http://ieeexplore.ieee.org/document/7762945/. in IEEE Transactions on
Knowledge and Data Engineering 201
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