Archive for the 'Big data' Category
December 28th, 2015, by Tim Finin, posted in Big data, cybersecurity, Datamining, Machine Learning, Security
Vehicles are becoming more and more connected, this opens up a larger attack surface which not only affects the passengers inside vehicles, but also people around them. These vulnerabilities exist because modern systems are built on the comparatively less secure and old CAN bus framework which lacks even basic authentication. Since a new protocol can only help future vehicles and not older vehicles, our approach tries to solve the issue as a data analytics problem and use machine learning techniques to secure cars. We develop a hidden markov model to detect anomalous states from real data collected from vehicles. Using this model, while a vehicle is in operation, we are able to detect and issue alerts. Our model could be integrated as a plug-n-play device in all new and old cars.
October 16th, 2015, by Tim Finin, posted in Big data, Machine Learning, NLP, NLP
Demystifying Word2Vec – A Hands-on Tutorial
10:30am Monday, 19 October 2015 **ITE 456**
In the world of NLP, Word2Vec is one of the coolest kids in town! But what exactly is it and how does it work? More importantly, how is it used/useful?
For the first 10-15 minutes, we will go over distributional an distributed representation of words and the neural language model behind Word2Vec. We will also briefly look at doc2vec, the extension of Word2Vec for longer pieces of text.
For the remainder of the time (45-60 minutes), we will get our feet wet by running Word2Vec on a dataset which will then be followed by discussions about potential ways it can be useful for your own work.
What to bring – Any computing machine with Python installed, lots of curiosity and some delicious snacks for me maybe? We will use the excellent gensim package for python to run Word2Vec along with cython to speed things up. If you aren’t familiar with Python or don’t like it, no worries! It’s really just 5-6 lines of code! The training dataset will be provided. If you wish to bring your own, that’s cool too.
NOTE: We will hold this week’s Ebiquity meeting in ITE 456.
June 6th, 2015, by Tim Finin, posted in Big data, Database, Machine Learning, RDF, Semantic Web
New paper: Lushan Han, Tim Finin, Anupam Joshi and Doreen Cheng, Querying RDF Data with Text Annotated Graphs, 27th International Conference on Scientific and Statistical Database Management, San Diego, June 2015.
Scientists and casual users need better ways to query RDF databases or Linked Open Data. Using the SPARQL query language requires not only mastering its syntax and semantics but also understanding the RDF data model, the ontology used, and URIs for entities of interest. Natural language query systems are a powerful approach, but current techniques are brittle in addressing the ambiguity and complexity of natural language and require expensive labor to supply the extensive domain knowledge they need. We introduce a compromise in which users give a graphical “skeleton” for a query and annotates it with freely chosen words, phrases and entity names. We describe a framework for interpreting these “schema-agnostic queries” over open domain RDF data that automatically translates them to SPARQL queries. The framework uses semantic textual similarity to find mapping candidates and uses statistical approaches to learn domain knowledge for disambiguation, thus avoiding expensive human efforts required by natural language interface systems. We demonstrate the feasibility of the approach with an implementation that performs well in an evaluation on DBpedia data.
June 5th, 2015, by Tim Finin, posted in Big data, KR, Machine Learning, Semantic Web
New paper: Zareen Syed, Tim Finin, Muhammad Rahman, James Kukla and Jeehye Yun, Discovering and Querying Hybrid Linked Data, Third Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data, held in conjunction with the 12th Extended Semantic Web Conference, Portoroz Slovenia, June 2015.
In this paper, we present a unified framework for discovering and querying hybrid linked data. We describe our approach to developing a natural language query interface for a hybrid knowledge base Wikitology, and present that as a case study for accessing hybrid information sources with structured and unstructured data through natural language queries. We evaluate our system on a publicly available dataset and demonstrate improvements over a baseline system. We describe limitations of our approach and also discuss cases where our system can complement other structured data querying systems by retrieving additional answers not available in structured sources.
May 17th, 2015, by Tim Finin, posted in Big data, Semantic Web
Transforming big data into smart data:
deriving value via harnessing volume, variety
and velocity using semantics and semantic web
Professor Amit Sheth
Wright State University
11:00am Tuesday, 26 May 2015, ITE 325, UMBC
Big Data has captured a lot of interest in industry, with the emphasis on the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity, and their applications to drive value for businesses. In this talk, I will describe Smart Data that is realized by extracting value from Big Data, to benefit not just large companies but each individual. If my child is an asthma patient, for all the data relevant to my child with the four V-challenges, what I care about is simply, "How is her current health, and what are the risk of having an asthma attack in her current situation (now and today), especially if that risk has changed?" As I will show, Smart Data that gives such personalized and actionable information will need to utilize multimodal data and their metadata, use domain specific knowledge, employ semantics and intelligent processing, and go beyond traditional reliance on Machine Learning and NLP. I will motivate the need for a synergistic combination of techniques similar to the close interworking of the top brain and the bottom brain in the cognitive models. I will present a couple of Smart Data applications in development at Kno.e.sis from the domains of personalized health, health informatics, social data for social good, energy, disaster response, and smart city.
Amit Sheth is an Educator, Researcher and Entrepreneur. He is the LexisNexis Ohio Eminent Scholar, an IEEE Fellow, and the executive director of Kno.e.sis – the Ohio Center of Excellence in Knowledge-enabled Computing a Wright State University. In World Wide Web (WWW), it is placed among the top ten universities in the world based on 10-year impact. Prof. Sheth is a well cited computer scientists (h-index = 87, >30,000 citations), and appears among top 1-3 authors in World Wide Web (Microsoft Academic Search). He has founded two companies, and several commercial products and deployed systems have resulted from his research. His students are exceptionally successful; ten out of 18 past PhD students have 1,000+ citations each.
Host: Yelena Yesha, yeyesha2umbc.edu
February 15th, 2015, by Tim Finin, posted in Big data
ACM Tech Talk
Studying Internet Latency via TCP Queries to DNS
Dr. Yannis Labrou
Principal Data Architect, Verisign
1:30-2:30pm Friday, 27 February 2015, ITE 456, UMBC
Every day Verisign processes upwards of 100 billion authoritative DNS requests for .COM and .NET from all corners of the earth. The vast majority of these requests are via the UDP protocol. Because UDP is connectionless, it is impossible to passively estimate the latency of the UDP-based requests. A very small percentage of these requests though, are over TCP, thus providing the means to estimate the latency of specific requests and paths for a subset of the hosts that interact with Verisign’s network infrastructure.
In this work, we combine this relatively small number of datapoints from TCP (on the order of a few hundred million per day) with the much larger dataset of all DNS requests. Our focus is the process of data analysis of real world, imperfect data at very large scale with the goals of understanding network latency at an unprecedented magnitude, identifying large volume, high latency clients and improving their latency. We discuss the techniques we used for data selection and analysis and we present the results of a variety of analyses, such as deriving regional and country patterns, estimations for query latency for different countries and network locations, and techniques for identifying high latency clients.
It is important to note that latency results we will report are based on passive measurements from, essentially, the entire Internet. For this experiment we do not have control over the client side — where they are, which software, their configuration, their network congestion. This is significantly different from latency studied in any active measurement infrastructure such as Planet Lab, RIPE Atlas, Thousand Eyes, Catchpoint, etc.
Dr. Yannis Labrou is Principal Data Architect at Verisign Labs where he leads efforts to create value from the wealth of data that Verisign’s operations generate every day. He brings to Verisign 20 years of experience in conceiving, creating and bringing to fruition innovations; combining thinking big with laboring through the pains of materializing ideas. He has done so in an academic environment, at a startup company, while conducting government and DoD/DARPA sponsored research and for a global Fortune 200 company.
Before joining Verisign, Dr. Labrou was a Senior Researcher at Fujitsu Laboratories of America, Director of Technology and member of the executive staff of PowerMarket, an enterprise application software start-up company and a Research Assistant Professor at UMBC. He received his Ph.D. in Computer Science from UMBC, where his research focused on software agents, and a Diploma in Physics from the University of Athens, Greece. He has authored more than 40 peer-reviewed publications, with almost 4000 citations and he has been awarded 14 patents from the USPTO. His current research focus is data through the entire lifecycle from generation to monetization.
— more information and directions: http://bit.ly/UMBCtalks —
January 14th, 2015, by Tim Finin, posted in Agents, AI, Big data, Ontologies, Semantic Web, Web
The theme of the 2015 Ontology Summit is Internet of Things: Toward Smart Networked Systems and Societies. The Ontology Summit is an annual series of events (first started by Ontolog and NIST in 2006) that involve the ontology community and communities related to each year’s theme.
The 2015 Summit will hold a virtual discourse over the next three months via mailing lists and online panel sessions augmented conference calls. The Summit will culminate in a two-day face-to-face workshop on 13-14 April 2015 in Arlington, VA. The Summit’s goal is to explore how ontologies can play a significant role in the realization of smart networked systems and societies in the Internet of Things.
The Summit’s initial launch session will take place from 12:30pm to 2:00pm EDT on Thursday, January 15th and will include overview presentations from each of the four technical tracks. See the 2015 Ontology Summit for more information, the schedule and details on how to participate in these free an open events.
January 3rd, 2015, by Tim Finin, posted in Big data, NLP
click image for higher-resolution version
Mark Liberman pointed out a nice use of pmi to explore the difference in meaning of geek vs. nerd done last year by Burr Settles using Twitter data.
Settles’s original post, On “Geek” Versus “Nerd”, has a brief, but good, explanation of the method and data.
July 15th, 2014, by Tim Finin, posted in Big data, KR, Ontologies, RDF, Semantic Web
In The trouble with DBpedia, Paul Houle talks about the problems he sees in DBpedia, Freebase and Wikidata and offers up :BaseKB as a better “generic database” that models concepts that are in people’s shared consciousness.
:BaseKB is a purified version of Freebase which is compatible with industry-standard RDF tools. By removing hundreds of millions of duplicate, invalid, or unnecessary facts, :BaseKB users speed up their development cycles dramatically when compared to the source Freebase dumps.
:BaseKB is available for commercial and academic use under a CC-BY license. Weekly versions (:BaseKB Now) can be downloaded from Amazon S3 on a “requester-paid basis”, estimated at $3.00US per download. There are also BaseKB Gold releases which are periodic :BaseKB Now snapshots. These can be downloaded free via Bittorrent or purchased as a Blu Ray disc.
It looks like it’s worth checking out!
February 26th, 2014, by Tim Finin, posted in Big data, Google
Google is offering a free, online MOOC style course on ‘Making Sense of Data‘ from March 18 to April 4 taught by Amit Deutsch (Google) and Joe Hellerstein (Berkeley).
Interestingly, it doesn’t require programming or database skills: “Basic familiarity with spreadsheets and comfort using a web browser is recommended. Knowledge of statistics and experience with programming are not required.” The course will use Google’s Fusion Tables service for managing and visualizing data
February 8th, 2014, by Tim Finin, posted in Big data, High performance computing, Ontologies, Semantic Web
In the first Ebiquity meeting of the semester, Vlad Korolev will talk about his work on using RDF for to capture, represent and use provenance information for big data experiments.
PROB: A tool for Tracking Provenance and Reproducibility of Big Data Experiments
10-11:30am, ITE346, UMBC
Reproducibility of computations and data provenance are very important goals to achieve in order to improve the quality of one’s research. Unfortunately, despite some efforts made in the past, it is still very hard to reproduce computational experiments with high degree of certainty. The Big Data phenomenon in recent years makes this goal even harder to achieve. In this work, we propose a tool that aids researchers to improve reproducibility of their experiments through automated keeping of provenance records.
January 18th, 2014, by Tim Finin, posted in Big data, Datamining, Machine Learning, Semantic Web
A free PDF version of the new second edition of Mining of Massive Datasets by Anand Rajaraman, Jure Leskovec and Jeffey Ullman is available. New chapters on mining large graphs, dimensionality reduction, and machine learning have been added. Related material from Professor Leskovec’s recent Stanford course on Mining Massive Data Sets is also available.
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