13,179 research outputs found
OpenMinTeD: A Platform Facilitating Text Mining of Scholarly Content
The OpenMinTeD platform aims to bring full text Open Access scholarly content from a wide range of providers together with Text and Data Mining (TDM) tools from various Natural Language Processing frameworks and TDM developers in an integrated environment. In this way, it supports users who want to mine scientific literature with easy access to relevant content and allows running scalable TDM workflows in the cloud
Grids and the Virtual Observatory
We consider several projects from astronomy that benefit from the Grid paradigm and
associated technology, many of which involve either massive datasets or the federation
of multiple datasets. We cover image computation (mosaicking, multi-wavelength
images, and synoptic surveys); database computation (representation through XML,
data mining, and visualization); and semantic interoperability (publishing, ontologies,
directories, and service descriptions)
Mining Threat Intelligence about Open-Source Projects and Libraries from Code Repository Issues and Bug Reports
Open-Source Projects and Libraries are being used in software development
while also bearing multiple security vulnerabilities. This use of third party
ecosystem creates a new kind of attack surface for a product in development. An
intelligent attacker can attack a product by exploiting one of the
vulnerabilities present in linked projects and libraries.
In this paper, we mine threat intelligence about open source projects and
libraries from bugs and issues reported on public code repositories. We also
track library and project dependencies for installed software on a client
machine. We represent and store this threat intelligence, along with the
software dependencies in a security knowledge graph. Security analysts and
developers can then query and receive alerts from the knowledge graph if any
threat intelligence is found about linked libraries and projects, utilized in
their products
The Ubiquity of Large Graphs and Surprising Challenges of Graph Processing: Extended Survey
Graph processing is becoming increasingly prevalent across many application
domains. In spite of this prevalence, there is little research about how graphs
are actually used in practice. We performed an extensive study that consisted
of an online survey of 89 users, a review of the mailing lists, source
repositories, and whitepapers of a large suite of graph software products, and
in-person interviews with 6 users and 2 developers of these products. Our
online survey aimed at understanding: (i) the types of graphs users have; (ii)
the graph computations users run; (iii) the types of graph software users use;
and (iv) the major challenges users face when processing their graphs. We
describe the participants' responses to our questions highlighting common
patterns and challenges. Based on our interviews and survey of the rest of our
sources, we were able to answer some new questions that were raised by
participants' responses to our online survey and understand the specific
applications that use graph data and software. Our study revealed surprising
facts about graph processing in practice. In particular, real-world graphs
represent a very diverse range of entities and are often very large,
scalability and visualization are undeniably the most pressing challenges faced
by participants, and data integration, recommendations, and fraud detection are
very popular applications supported by existing graph software. We hope these
findings can guide future research
- âŠ