International world wide web conference

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43
TUT
ORIALS
TUESDAY
 APRIL 12
BUILDING DECENTRALIZED APPLICATIONS FOR THE SOCIAL WEB  
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  ROOM 519A
9:00 am  –  5:30 pm
PRESENTERS
ANDREI SAMBRA, MIT/W3C
AMY GUY, UNIVERSITY OF EDINBURGH/MIT
SARVEN CAPADISLI, UNIVERSITY OF BONN/MIT
NICOLA GRECO, MIT
ABSTRACT
Recent advancements in technologies and protocols mean that it is easier than ever to integrate social features 
into diverse web applications, and increased awareness of privacy concerns means that it is pertinent to con-
sider empowerment of application users when doing so. Many developers are already familiar with the notion 
of personal data stores; this tutorial will demonstrate how to access or provide such stores for users, and build 
simple web applications which read and write to the storage whilst remaining completely decoupled from it. This 
advantages developers in two ways: by removing the burden of storing and maintaining a canonical copy of user 
data; and by enabling access to and ease of integration with data created through other applications, creating 
richer, seamless experiences. From the application users’ perspective, they need no longer commit and become 
bound to particular services, but can mix, match and move between those that best meet their needs.
We will introduce Solid, a set of protocols based on existing W3C recommendations, for reading, writing and 
access control of the contents of a personal data store, which can be layered up in order to integrate various 
social features into new or existing web applications. Attendees will leave with an understanding of Solid and 
how different parts of the protocols can work together, and having written some code to implement the parts 
that interest them most. They will also have hands on experience with existing libraries and tooling to facilitate 
working with the Solid protocols. Those who stay for the full day will have an opportunity to build a small but 
complete web application with decentralized social features, and to collaborate with others to see the advantag-
es of sharing data between multiple applications.

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  I N T E R N AT I O N A L   W O R L D   W I D E   W E B   C O N F E R E N C E
MINING BIG TIME-SERIES DATA ON THE WEB  
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  ROOM 519B
9:00 am  –  12:30 pm
PRESENTERS
YASUSHI SAKURAI, KUMAMOTO UNIVERSITY
YASUKO MATSUBARA, KUMAMOTO UNIVERSITY 
CHRISTOS FALOUTSOS, CARNEGIE MELLON UNIVERSITY
ABSTRACT
Online news, blogs, SNS and many other Web-based services has been attracting considerable interest for 
business and marketing purposes. Given a large collection of time series, such as web-click logs, online search 
queries, blog and review entries, how can we efficiently and effectively find typical time-series patterns? What are 
the major tools for mining, forecasting and outlier detection? Time-series data analysis is becoming of increasing-
ly high importance, thanks to the decreasing cost of hardware and the increasing on-line processing capability.
The objective of this tutorial is to provide a concise and intuitive overview of the most important tools that 
can help us find meaningful patterns in large-scale time-series data. Specifically we review the state of the art 
in three related fields: (1) similarity search, pattern discovery and summarization, (2) non-linear modeling and 
forecasting, and (3) the extension of time-series mining and tensor analysis. We also introduce case studies that 
illustrate their practical use for social media and Web-based services.
AUTOMATIC ENTITY RECOGNITION AND TYPING IN MASSIVE TEXT CORPORA  
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  ROOM 521ABC
9:00 am  –  12:30 pm
PRESENTERS
XIANG REN, UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN, URBANA
AHMED EL-KISHKY, UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN, URBANA
CHI WANG, MICROSOFT RESEARCH 
JIAWEI HAN, MICROSOFT RESEARCH
ABSTRACT
In today’s computerized and information-based society, we are soaked with vast amounts of natural language 
text data, ranging from news articles, product reviews, advertisements, to a wide range of user-generated content 
from social media. To turn such massive unstructured text data into actionable knowledge, one of the grand chal-
lenges is to gain an understanding of entities and the relationships between them. In this tutorial, we introduce 
data-driven methods to recognize typed entities of interest in different kinds of text corpora (especially in mas-
sive, domain-specific text corpora). These methods can automatically identify token spans as entity mentions in 
text and label their types (e.g., people, product, organization) in a scalable way. We demonstrate on real datasets 
including news articles and yelp reviews how these typed entities aid in knowledge discovery and management.

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45
TUT
ORIALS
ANALYZING SEQUENTIAL USER BEHAVIOR ON THE WEB  
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  ROOM 519B
2:00 pm  –  5:30 pm
PRESENTERS
PHILIPP SINGER, LEIBNIZ INSTITUTE FOR THE SOCIAL SCIENCES GESIS
FLORIAN LEMMERICH, LEIBNIZ INSTITUTE FOR THE SOCIAL SCIENCES GESIS
ABSTRACT
The World Wide Web is an information environment that facilitates sequential user behavior between states. A 
prime example for that is the navigation of users between websites enabled through the presence of hyperlinks. 
However, today, we can think of many other kinds of transitional behavior that many of us perform on a daily 
base. For instance, if users listen to music on Spotify, they transition between songs, or when users check-in at 
locations on Foursquare they transition between geocoordinates, or when users write reviews on Amazon they 
transition between products.
To that end, we consider all kinds of transitions between states as sequences on the Web. States can refer to any 
kind of categorical action performed, such as the ones listed. Our research community has been interested in 
studying such sequences in various contexts such as (i) modeling, (ii) the detection of regularities and patterns 
or (iii) the understanding of the production of underlying sequences (e.g., cognitive strategies). Recent research 
heavily focused on studying human navigation on the Web, but also other types of transition data have sparked 
the interest of researchers such as mobility sequences, search sequences or song listening sequences. In this tuto-
rial we will give an outline of the fundamental methods of analyzing such categorical sequences on the Web and 
discuss some recent advancements in-depth.

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