8,458 research outputs found
Fast Search for Dynamic Multi-Relational Graphs
Acting on time-critical events by processing ever growing social media or
news streams is a major technical challenge. Many of these data sources can be
modeled as multi-relational graphs. Continuous queries or techniques to search
for rare events that typically arise in monitoring applications have been
studied extensively for relational databases. This work is dedicated to answer
the question that emerges naturally: how can we efficiently execute a
continuous query on a dynamic graph? This paper presents an exact subgraph
search algorithm that exploits the temporal characteristics of representative
queries for online news or social media monitoring. The algorithm is based on a
novel data structure called the Subgraph Join Tree (SJ-Tree) that leverages the
structural and semantic characteristics of the underlying multi-relational
graph. The paper concludes with extensive experimentation on several real-world
datasets that demonstrates the validity of this approach.Comment: SIGMOD Workshop on Dynamic Networks Management and Mining (DyNetMM),
201
Crowdsourcing Cybersecurity: Cyber Attack Detection using Social Media
Social media is often viewed as a sensor into various societal events such as
disease outbreaks, protests, and elections. We describe the use of social media
as a crowdsourced sensor to gain insight into ongoing cyber-attacks. Our
approach detects a broad range of cyber-attacks (e.g., distributed denial of
service (DDOS) attacks, data breaches, and account hijacking) in an
unsupervised manner using just a limited fixed set of seed event triggers. A
new query expansion strategy based on convolutional kernels and dependency
parses helps model reporting structure and aids in identifying key event
characteristics. Through a large-scale analysis over Twitter, we demonstrate
that our approach consistently identifies and encodes events, outperforming
existing methods.Comment: 13 single column pages, 5 figures, submitted to KDD 201
From Query-By-Keyword to Query-By-Example: LinkedIn Talent Search Approach
One key challenge in talent search is to translate complex criteria of a
hiring position into a search query, while it is relatively easy for a searcher
to list examples of suitable candidates for a given position. To improve search
efficiency, we propose the next generation of talent search at LinkedIn, also
referred to as Search By Ideal Candidates. In this system, a searcher provides
one or several ideal candidates as the input to hire for a given position. The
system then generates a query based on the ideal candidates and uses it to
retrieve and rank results. Shifting from the traditional Query-By-Keyword to
this new Query-By-Example system poses a number of challenges: How to generate
a query that best describes the candidates? When moving to a completely
different paradigm, how does one leverage previous product logs to learn
ranking models and/or evaluate the new system with no existing usage logs?
Finally, given the different nature between the two search paradigms, the
ranking features typically used for Query-By-Keyword systems might not be
optimal for Query-By-Example. This paper describes our approach to solving
these challenges. We present experimental results confirming the effectiveness
of the proposed solution, particularly on query building and search ranking
tasks. As of writing this paper, the new system has been available to all
LinkedIn members
Reverse spatial visual top-k query
With the wide application of mobile Internet techniques an location-based services (LBS), massive multimedia data with geo-tags has been generated and collected. In this paper, we investigate a novel type of spatial query problem, named reverse spatial visual top- query (RSVQ k ) that aims to retrieve a set of geo-images that have the query as one of the most relevant geo-images in both geographical proximity and visual similarity. Existing approaches for reverse top- queries are not suitable to address this problem because they cannot effectively process unstructured data, such as image. To this end, firstly we propose the definition of RSVQ k problem and introduce the similarity measurement. A novel hybrid index, named VR 2 -Tree is designed, which is a combination of visual representation of geo-image and R-Tree. Besides, an extension of VR 2 -Tree, called CVR 2 -Tree is introduced and then we discuss the calculation of lower/upper bound, and then propose the optimization technique via CVR 2 -Tree for further pruning. In addition, a search algorithm named RSVQ k algorithm is developed to support the efficient RSVQ k query. Comprehensive experiments are conducted on four geo-image datasets, and the results illustrate that our approach can address the RSVQ k problem effectively and efficiently
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