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2019 Proceeding- Editors:
- Akhil Arora,
- Arnab Bhattacharya,
- George Fletcher
We are delighted to present the papers from the 2nd GRADES-NDA Joint Workshop on Graph Data Management Experiences & Systems and Network Data Analytics, which took place on 30th June, 2019 co-located with the ACM SIGMOD conference in Amsterdam, Netherlands. GRADES-NDA 2019 is the second joint meeting of the GRADES and NDA workshops, which were each independently organized at previous SIGMOD-PODS meetings, GRADES since 2013 and NDA since 2016.The focus of GRADES-NDA is the application areas, usage scenarios and open challenges in managing large-scale graph-shaped data. The workshop is a forum for exchanging ideas and methods for mining, querying, and learning with real-world network data, developing new common understandings of the problems at hand, sharing of data sets and benchmarks where applicable, and leveraging existing knowledge from different disciplines. GRADES-NDA aims to present technical contributions inside graph, RDF, and other data management systems on massive graphs.
The purpose of this workshop is to bring together researchers from academia, industry, and government, (1) to create a forum for discussing recent advances in (large-scale) graph data management and analytics systems, as well as propose and discuss novel methods and techniques towards (2) addressing domain specific challenges or (3) handling noise in real-world graphs.
Proceeding Downloads
Length Spectrum Theory, Non-backtracking Cycles, and Two Graph Analysis Tasks
Two basic tasks in graph analysis are: (1) computing the distance between two graphs and (2) embedding of the graph elements (i.e., nodes or links) into a lower-dimensional space. The former task has numerous applications from k-nearest neighbor search, ...
We Don't Need No Education: From Building for Coders to Building for Users
Since the inception of data management research in the 70s, both academic and commercial efforts have been heavily skewed toward query paradigms that are based on textual query languages (e.g., SQL, XQuery, SPARQL). The underlying implicit (and ...
It's Just Graph
Graph has emerged as a hot topic in many different communities (RDF/SPARQL, Property Graph (Gremlin / Cypher / PGQL), graph embeddings, graph learning, linear algebra, etc. I will look back over 20 years of Graph at different conceptual approaches and ...
A Linked Data Quality Assessment Framework for Network Data
For network analysts, understanding how traffic flows through a network is crucial to network management and forensics such as network monitoring, vulnerability assessment and defence. In order to understand how traffic flows through a network, network ...
Graph Traversals for Regular Path Queries
Regular Path Queries (RPQs) are at the core of many recent declarative graph pattern matching languages. They leverage the compactness and expressiveness of regular expressions for matching recursive path structures. Unfortunately, most prior works on ...
Defining Schemas for Property Graphs by using the GraphQL Schema Definition Language
GraphQL is a highly popular new approach to build Web APIs. An important component of this approach is the GraphQL schema definition language (SDL). The original purpose of this language is to define a so-called GraphQL schema that specifies the types ...
Experiences with Implementing Landmark Embedding in Neo4j
Reachability, distance, and shortest path queries are fundamental operations in the field of graph data management with various applications in research and industry. However, while various preprocessing-based methods have been proposed to optimize the ...
Fast Concurrent Reads and Updates with PMAs
Fast navigation through graphs with O(1) cost relies on compact storage of graphs in dense arrays, but is not efficiently updatable. In this paper we propose storage of updatable graphs in Packed Memory Arrays (PMAs), and tackle the problem of ...
Cut to Fit: Tailoring the Partitioning to the Computation
Graph analytics applications are very often built using off-the-shelf analytics frameworks, which are profiled and optimized for the general case and have to perform for all kind of graphs. As performance is affected by the selection of the partition ...
Fast and Accurate Entity Linking via Graph Embedding
Entity Linking, the task of mapping ambiguous Named Entities to unique identifiers in a knowledge base, is a cornerstone of multiple Information Retrieval and Text Analysis systems. So far, no single entity linking algorithm has been able to offer the ...
SIS Contagion Avoidance on a Network Growing by Preferential Attachment
The economic and convenience benefits of interconnectivity drive the current explosive growth in networked systems. However, as recent catastrophic contagious failures in numerous large-scale networked infrastructures have demonstrated, ...
Evaluation of the Context-Free Path Querying Algorithm Based on Matrix Multiplication
- Nikita Mishin,
- Iaroslav Sokolov,
- Egor Spirin,
- Vladimir Kutuev,
- Egor Nemchinov,
- Sergey Gorbatyuk,
- Semyon Grigorev
Recently proposed matrix multiplication based algorithm for context-free path querying (CFPQ) offloads the most performance-critical parts onto boolean matrices multiplication. Thus, it is possible to achieve high performance of CFPQ by means of modern ...
Leveraging Twitter and Neo4j to Study the Public Use of Opioids in the USA
In this paper, we present a use case of graph data management systems in the health care domain. Basically, we used Neo4j as a platform to store and analyze tweets that mention at least one opioid-related keyword. Opioid (mis)use is an escalating public-...
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Acceptance Rates
| Year | Submitted | Accepted | Rate |
|---|---|---|---|
| GRADES-NDA'20 | 15 | 9 | 60% |
| GRADES-NDA'19 | 20 | 10 | 50% |
| GRADES-NDA '18 | 26 | 10 | 38% |
| Overall | 61 | 29 | 48% |