Editor: Schahram Dustdar â€¢ [email protected]
62 Published by the IEEE Computer Society 1089-7801/13/$31.00 Â© 2013 IEEE IEEE INTERNET COMPUTING
n recent years, the quantity of information
generated by business, government, and science has increased immensely â€” a phenomenon known as the data deluge. In business,
Walmartâ€™s transactional databases are estimated
to contain more than 2.5 petabytes of data consisting of customer behaviors and preferences,
network and device activity, and market trends
data.1 In the military, US Air Force drones collected approximately 24 yearsâ€™ worth of video
footage from Afghanistan and Iraq in 2009.1 In
science, the Large Hadron Collider (LHC) facility
at CERN produced 13 petabytes of data in 2010.2
Moreover, sensor, social media, mobile, and
location data are growing at an unprecedented
rate. In parallel to this significant growth, data
are also becoming increasingly interconnected.
Facebook, for instance, is nearly fully connected,
with 99.91 percent of individuals on the social
network belonging to a single, large connected
component (see http://arxiv.org/abs/1111.4503).
This astonishing growth and diversity have
profoundly affected how people process and
interpret new knowledge. Because most of this
data both originates and resides in the Internet,
one open challenge is determining how Internet computing technology should evolve to let
us access, assemble, analyze, and act on big
data. We believe that data are first-class citizens
in the Internet landscape. The collaborative
interplay between data and computation infrastructure is vital for enabling low-latency and
high-throughput analytics on big data.
Advances in social networks and analytics span many Internet-based computing paradigms, including cloud and services computing.3
Currently, most social networks connect people
or groups who expose similar interests or features. In the near future, we expect that such
networks will connect other entities, such as
software components, Web-based services, data
resources, and workflows. More importantly, the
interactions among people and nonhuman artifacts have significantly enhanced data scientistsâ€™
productivity. Big data analytics can accumulate the wisdom of crowds, reveal patterns, and
yield best practices. For a real-world example,
in recent events related to the 2013 Boston
Marathon bombings, social networks of marathon participants and general high-performance
computational techniques were combined to
cluster and analyze large sets of candid photos
and video shots â€” ultimately leading to the discovery of the perpetrators. This example exemplifies how cloud-oriented processing techniques
can meet computational needs, while analytics
are enhanced by the special expertise of social
Big Data Analytics
Wei Tan â€¢ IBM T.J. Watson Research Center
M. Brian Blake and Iman Saleh â€¢ University of Miami
Schahram Dustdar â€¢ Vienna University of Technology
Very large datasets, also known as big data, originate from many domains.
Deriving knowledge is more difficult than ever when we must do it by intricately processing this big data. Leveraging the social network paradigm could
enable a level of collaboration to help solve big data processing challenges.
Here, the authors explore using personal ad hoc clouds comprising individuals
in social networks to address such challenges.
Social-Network-Sourced Big Data Analytics
SEPTEMBER/OCTOBER 2013 63
The astonishing growth and
diversity in connected data continues to profoundly affect how people
make sense of this data. We can
define this interplay as a virtuous
circle in which
â€¢ connected people produce a continuous data stream thatâ€™s deposited into a repository of connected
â€¢ individuals or business entities
might conduct big data analytics
on these connected data by leveraging ad hoc clouds or connected
â€¢ analytics on the big data from
these connected computers generates intelligence that subsequently proliferates back to
As Figure 1 illustrates, this system is
continually evolving, as is the knowledge that the interaction generates.
Here, we show that the collaborative interplay of connected computers and connected people has opened
new avenues with regard to how
humans interpret connected data. In
fact, connected data is the confluence
where social networks and clouds are
presented as a solution for big data
Connected People: Social
Networks and Big Data
Recent social networking websites
such as Twitter, Facebook, LinkedIn,
YouTube, and Wikipedia have not only
connected large user populations but
have also captured exabytes of information associated with their daily
interactions. Social networking has its
beginnings in the work of social scientists in the context of human social
networks, mathematicians and physicists in the context of complex network
theory, and, most recently, computer
scientists in the examination of information or Internet-enabled social networks.4 We can thus separate major
research challenges into these areas.
Humanistic Social Networks
Stemming back to the 1920s, social
scientists have investigated interpersonal relationships as they relate to the
larger network topography of societal
groups of interrelated humans. These
studies have attempted to systematically devise relationshipsâ€™ strength
and have implicitly determined how
trust plays into those relationshipsâ€™
interconnections. In managing these
networks, social scientists and sociologists have employed several methods.5
Modeling approaches include
network-oriented data collection, block
modeling, network-oriented data sampling, diffusion models, and models
for longitudinal or emerging data.
Measurements include centrality measures for groups, cross-network assessment or correspondence analysis for
two-mode networks, and statistical
assessment of the p* model.
Complex Network Theory
Mathematicians and physicists perform some of the same analysis as
social scientists but concentrate on
the network structureâ€™s more quantitative aspects.6 The emergence of
social behavior is derived from the
natural quantitative connections
between nodes and links within a
network. Given that network structure
is irregular, complex, and dynamically evolving in time, the main
focus for complex network theory
is the development of principled,
mathematical approaches that assess
networks of millions of nodes. Furthermore, mathematicians and physicists derive insight from biological
systems that form in nature. A significant vehicle for deriving these
networksâ€™ behavior is the analysis
of path lengths and the clustering
of related path structures. Complex networks can be represented
in their most fundamental forms
as graphs or small-world networks,
but more intricate topographies are
represented as weighted, random,
power-law, or spatial networks. One
common approach for managing
these networks thatâ€™s shared with
computer scientists is spectral graph
partitioning, which determines the
minimal number of edges between
two sets of vertexes within a graph.
Hierarchical clustering is an effective method for networks in which
a priori knowledge of the number
of communities is lacking. This
approach attempts to divide nodes
into clusters where the connections
within the cluster are more closely
Figure 1. The virtuous circle. Connected people produce a data stream thatâ€™s
analyzed by connected computers, and the intelligence such an analysis
generates proliferates back to connected people.
The model of connected people,
software, services, and physical entities
– On-demand computation power
– Storage and analytics of big and
– Social networks
– Wisdom of the crowds
deriving connected data
64 www.computer.org/internet/ IEEE INTERNET COMPUTING
related than the connections to
nodes assigned to a different cluster.
Other approaches attempt to look for
the largest distance between nodes
until clusters are naturally formed.
and Social Networking
Computer scientists and information
engineers have combined the initial
work on social and complex networks
and mapped them onto networks
representing information-systemsoriented environments. Many studies
investigate a fundamental question:
â€œDo online social networks resemble
or behave in similar ways as people
in real-world situations?â€ Computer
scientists have employed hybrid assessment approaches similar to the traditional methods used in sociology and
computational sciences. Web graph
analysis, for instance, attempts to integrate the nuances of the Web when
considering network analysis.
Social Networks as Big Data
Understanding social networks evolves
into a big data problem when business, management, or information
systems specialists hope to predict
behavior to ultimately enhance marketing, sales, and online commerce.
Many social networking sites have
between 10 and 200 million users,
so data sampling is central to most
studies. Although significantly timeconsuming, gaining insight from the
entire dataset might provide the most
optimal solutions. Big data is usually
characterized by the â€œthree Vsâ€ â€” that
is, volume, velocity, and variety.7 In
terms of volume, at the end of 2011,
Facebook had 721 million individuals and 68.7 billion friendship edges
(see http://arxiv.org/abs/1111.4503). In
terms of velocity, Twitter and Facebook respectively generate 7 Tbytes
and 10 Tbytes of data daily. These
data also need to be processed at the
speed of thought. For example, on
11 November 2012, a sales event at
TaoBao, the largest online shopping
marketplace in China, generated 100
million transactions and reached a
peak transaction rate of 205,000 per
minute (see http://tech.sina.com.cn/i/
terms of variety, data today come from
various sources, ranging from surveillance videos, to satellite images, to
mobile tweets, to sensors and meters
in the power grid.
Given the astonishing amount of
data being produced and the need to
store and process them economically,
organizations are widely adopting
scale-out rather than scale-up systems to acquire and interpret data.
Key features of the scale-out pattern
include commodity server clusters,
share-nothing architecture (no shared
memory, storage, and so on), a TCP/
IP network connection, and a parallel programming framework such as
MapReduce. Cloud computing, which
offers scale-out and on-demand computing resources in a pay-per-use
manner, is an ideal technology to
enable big data for mainstream uses.
For example, Netflix stores movies
and TV shows, and Dropbox stores
customersâ€™ files, both in Amazonâ€™s
Simple Storage Service (S3). Yelp not
only uses Amazonâ€™s storage but also
Amazon Elastic MapReduce to power
its user-behavior analytics. Microsoft
Windows Azure and IBM SmartCloud
Enterprise+ offer similar functions.
Startup companies such as Cloudera,
Hortonworks, and MapR Technologies
are building value-added software
and solutions on top of the Apache
In recent years, scale-out data
stores, popularly referred as NoSQL
systems,8 are rapidly gaining popularity as a potential solution to support Internet-scale applications. These
stores include commercial systems
such as Amazonâ€™s DynamoDB, Googleâ€™s
BigTable, and Yahooâ€™s PNUTS, as well
as open source ones such as Cassandra,
HBase, and MongoDB. These stores
usually provide limited APIs (create,
read, update, and delete operations)
compared to relational databases, and
focus on scalability and elasticity on
commodity hardware. Such platforms
are particularly attractive for applications that perform relatively simple
operations while needing low-latency
guarantees as they scale to large sizes.
NoSQL stores offer flexible schema
and elasticity to overcome relational
databasesâ€™ limitations. However, in
doing so, they trade off full ACID
guarantees. Clearly, several challenges
exist for computational systems that
process big data.
Data Models and
Relational models and SQL provide
an abstraction layer between the
databaseâ€™s physical layer and the
application layer. This feature lets
users specify a query in a languagedependent and declarative manner,
while a query engine schedules and
optimizes its execution. No similar
solution exists for big data analysis.
Instead, NoSQL data stores offer various forms of data structures â€” such
as document, graph, row-column,
and key-value pair â€” that are directly
exposed to users. So, users must
understand dataâ€™s physical organization and employ vendor-specific
APIs to manipulate these data. Current state of the art attempts to
devise a SQL layer on top of NoSQL,
but without an abstract data model,
this effort is ad hoc and limited to
the underlying technology.
and Approximate Result
Volume and velocity impose contradictory requirements on big data systems. A large volume of data is injected
into such a system at a high speed,
while analysis and interpretation must
occur at the same pace. In traditional
business intelligence (BI) analytics,9
Social-Network-Sourced Big Data Analytics
SEPTEMBER/OCTOBER 2013 65
transactional data is processed initially on an online transaction processing (OLTP) system before flowing
through an extract, transform, load
(ETL) process in a batch mode. Eventually, data are loaded into an online
analytical processing (OLAP) data
warehouse, where theyâ€™re analyzed to
provide strategic insights. This OLTPETL-OLAP approach trades timeliness
for accuracy, given that a long delay
occurs between when data becomes
available and insight generation.
In some big data applications,
such as financial fraud detection and
market promotion, long delays arenâ€™t
tolerable. A newly emerged paradigm
called stream computing enables continuous queries over streaming data
such as social media feeds and call
data records. Stream computing opens
a gateway to real-time analytics, but
a few challenges remain. One is
the interplay between building the
batch mode model and sensing the realtime streams. On one hand, the accumulated historical data in the data
warehouse can help information specialists build a statistical model to
guide stream processing â€” for example, decide which features to observe
and help set the reacting threshold.
On the other hand, the newly arrived
data from the stream system should
be leveraged to tune the model to
reflect the recent trends. An incremental data processing and modeltuning mechanism is vital to this
With respect to the volume-velocity challenges, another perspective is
to provide approximate, just-in-time
results to queries, or prioritize different queries by allocating a varying
amount of resources.10 As such, different data consistency levels are possible
in which queries can be either accurate
but slow or best-effort but fast.
NoSQL, Scalable SQL, and NewSQL
To address the big data challenge,
NoSQL proponents limit ACID
constraints, provide fully scalable
solutions with preliminary database
features, and then slowly add back
the relational database management
system (RDBMS) features such as
index and transaction support. We
can observe this trend in Googleâ€™s
BigTable to Spanner evolution.
On the other end of the spectrum,
the RDBMS community is rethinking
its systemsâ€™ design and is attempting to
scale them in a share-nothing environment. These approaches add the ability to autopartition and autoscale data
while offering more options for trading off consistency for performance.
Moreover, other NewSQL11 projects
seek to modernize the RDBMS architecture to provide the same scalable
performance of NoSQL while preserving the ACID guarantees of a traditional, single-node database system.
Connected Data: New
Challenges for Clouds and
Research has shown that users primarily employ social networking
sites to articulate and make visible
their existing social networks.12,13
In other words, users on these sites
arenâ€™t usually trying to connect with
strangers but are primarily communicating with people who are already
part of their direct or extended social
network. This observation implies
that a level of trust already exists
between social network users, and
that these users share at least one
aspect of their lives: career, hobbies,
political views, and so on. We envision that these characteristics are
vital to enabling interesting opportunities, including establishing security
policies that leverage existing trust
relationships, promoting data and
resource sharing within networks
of people with similar interests, and
optimizing data analytics by leveraging the fact that people in the
same network potentially share the
same interests and will thus submit
similar queries. Finally, we propose
leveraging the wisdom of socially
connected individuals to build and
maintain service reputation systems.
Clouds comprising social network
connections open numerous research
Social networking on the cloud
could enable resource sharing based
on the social relationship between
users. This would potentially build on
technologies such as volunteer computing, which is a distributed computing model in which connected users
donate computing resources to a project. [email protected] and Boinc15 are
two examples. In these cases, the computing resources are owned by individuals and can be shared in return
for access to other resources. This
could potentially change the cloudâ€™s
economics and raises questions
related to reliability and quality-ofservice (QoS) guarantees. Again, we
can leverage the social aspect to build
reputation for users and establish their
corresponding resource reliability.
Locality of Reference
in the Cloud
The cloudâ€™s big data aspect constitutes
a challenge for both efficient data
analysis and mining. From a performance perspective, the cloudâ€™s social
aspect can be leveraged to compute,
cache and share the analytics results
within a circle of connected users.
These users are potentially interested
in the same patterns, so computations would exhibit high locality of
reference, which can help to optimize
Privacy-Preserving Data Analytics
On the other hand, privacy-preserving statistical techniques, such as differential privacy, can be employed in
conjunction with social links to maximize query result accuracy without
revealing private data. Privacy levels and accuracy can be defined differently within a social setting. For
example, privacy constraints can be
66 www.computer.org/internet/ IEEE INTERNET COMPUTING
relaxed depending on the number
of links between sets of users in a
social graph. Differential privacy
techniques must also be refined to
deal with incremental data that has
Cross-Domain Data Analytics
Aggregating data from multiple
social networks enables data analytics that correlate the datasetsâ€™ various
networks. Given that social networking vocabulary varies from one network to another, we anticipate the
need for cross-domain vocabulary
mapping as a data preprocessing step.
For example, the Twitter glossary
defines terms such as â€œfollowersâ€ and
â€œtweet.â€ Facebook defines terms such
as â€œfriendsâ€ and â€œstatus.â€ Google Plus
uses â€œcirclesâ€ and â€œhangout.â€ To perform cross-domain data analytics, we
must develop and maintain a common ontology that will capture the
differences and similarities in terminologies and define relationships
between terms within and across the
Socializing Access Control Policies
Security is a major concern that we
must address when coupling social
networks with the cloud. User groups,
roles, and access control policies must
be in place to govern usersâ€™ access to
cloud resources. To facilitate this process, we could leverage social relationships to build an evolving access
control system that self-adapts to
the addition, deletion, and update in
users and their relationships. Some
work has proposed semantically
annotating these relationships and
using semantically described rules
to infer relationships between users
and resources.16â€“18 These relationships can then help to establish trust
and form the basis of access control
policies. Because cloud resources are
largely dynamic, self-adapting policy
rules are needed to determine usersâ€™
access rights as new resources become
available and new users connect to
the social network. These rules can
use just-in-time data classification
schemes to infer access rules for new
data items as theyâ€™re digitally born
within the cloud. As Figure 2 shows,
the outcome is a social graph overlaid with security groups and policies;
based on their social links, new users
can be automatically classified into
groups as they join the network.
Service Reputation Frameworks
Cloud computing reaches its potential when software is implemented
as services that can be mixed and
matched over the cloud to address
usersâ€™ requirements. Automatic service discovery and composition can
occur based on servicesâ€™ reputation.
A service reputation can be built
from usersâ€™ feedback and by auditing a service invocation and execution. The service reputation is hence
a function of both the QoS a service
delivers, measured over the historical execution log, and the explicit
Some generic frameworks propose
incorporating service reputation as a
selection criterion when composing
services.19 Incorporating the social
dimension can largely enrich these
frameworks. Consider a travel reservation website that composes and
invokes different services to find the
best deals on air tickets. By binding
this functionality to a social network,
not only can we effectively build a service reputation by incorporating community wisdom, but a consensus for
evaluating services will exist among
users because theyâ€™re potentially of
the same mindset. For example, some
communities would appreciate price
over the length of a flight, others a
serviceâ€™s response time over result
quality. Consequently, the reputation
value calculated within social settings
is a more accurate measure of satisfaction within a user community.
The success of Facebook and LinkedIn
demonstrates that the Webâ€™s power
can not only foster but can also
capitalize on a social network. Such
Figure 2. Overlaying the social graph with security groups, roles, and policies.
Based on their social links, new users can be automatically classified into
groups as they join the network.
Social-Network-Sourced Big Data Analytics
SEPTEMBER/OCTOBER 2013 67
networks, both for the general public and specifically for the scientific
community, are changing user communication and practices. We classify all social networks using two
criteria: level of generality and ability to execute.20 In the level of generality dimension, we distinguish
a social network for general and
specific purposes. In the ability to
execute dimension, we distinguish
informative and executable (that is,
able to run computation) social networks. We show this classification
in light of scientific networks, but it
applies to nonscientific ones as well.
Informative vs. Executable
When considering the overlap of
social networking techniques and
commodity or cloud computation, a
distinct difference exists between
the system being informative or
General-purpose social networking sites have aspects of both:
â€¢ Informative. General-purpose social
networks such as Facebook and
LinkedIn have been harnessed to
cultivate communication and collaboration.2 For example, major
scientific associations such as
the American Association for the
Advancement of Science (AAAS)
and the IEEE have set up groups
on both Facebook and LinkedIn.
In these major community groups
and many smaller ones, members
can share research progress, search
for jobs, and seek collaborations.
â€¢ Executable. Besides these informative social networks, many
websites provide open and collaborative platforms to search
for executable mashups, Web
services, and so on. This category includes ProgrammableWeb
an online community for Web
APIs and mashups, and Amazon Elastic Compute Cloud (EC2;
Research-oriented social networks tend to be naturally integrated
with informativeness and execution
â€¢ Informative. Various social networking sites exist for general
academia, such as CiteULike (www.
citeulike.org) and Nature Network
websites are based on author-publication-citation networks and can
be used to identify connections
among authors, publications, and
research topics. Sites also exist
for specific communities, such as
life scientists (http://prometeonet
work.com) and doctors (www.doc
â€¢ Informative-executable. Many sites
go beyond just bringing people
together. Rather, they enable
researchers to share data and
protocols that describe methodologies for conducting experiments
and obtaining data. OpenWetWare
(http://openwetware.org) is such
an example for biology.
â€¢ Executable. Some research-specific
social networks are computationoriented â€” that is, they facilitate
the sharing of executable computational components. For example,
ment.org) offers a curated registry
of scientific workflows and a platform on which to execute them;
nanoHub21 provides a nanotechnology research gateway hosting
not only user groups and tutorials,
but also simulation tools.
Figure 3 lists social networks for
scientists. Each one is positioned
based on its relative level of generality
(the x-axis) and ability to execute (the
Figure 3. Social networks for scientists. Each network is positioned based on
its relative level of generality and its ability to execute. (Some online services
included in this figure, such as Amazon EC2, Globus Online, Galaxy, and
caGrid, are arguably social networks by themselves. However, we list them
here because they all provide an open collaborative environment thatâ€™s very
close to a social network and can rapidly evolve toward that direction.)
Ability to execute
Amazon EC2 Globus Online
Level of generality
68 www.computer.org/internet/ IEEE INTERNET COMPUTING
y-axis). To understand how big data
research is overlapping with cloud
computing research, Figure 4 shows a
word cloud generated from more than
60 recent research papers on cloud
computing and big data in the last
two years. Based on the frequency of
words, we can see that resource management and performance issues are
gaining the communityâ€™s attention.
Technologies such as MapReduce
and Hadoop are becoming the leading examples in this field. Research
has also started addressing energy
issues related to the cloud. Interestingly, social and mobile domains
arenâ€™t gaining the expected attention
despite the popularity of social networking and mobile devices.
With beginnings in social science,
mathematics, physics, and now
computer science, social interactions
among humans have been widely investigated. However, the vast amount
of data available in digital form,
coupled with larger, well-organized
groups of users, facilitate a significant
enhancement in collective human intelligence and knowledge derived from
collective data. We can summarize
this as the overlap of social networks
for big data analysis. This area presents a wealth of new research opportunities for engineers and scientists.
Engineers will need to introduce
new distributed data analysis frameworks in which users have access to
subsets of the â€œbig dataâ€ datasets as
well as situational awareness into
global processing. This framework
should enable engineers to share computational resources while leveraging
them on desktops, servers, and mobile
phones. Big data analysis over clouds
canâ€™t be done by trial and error, but
rather will require just-in-time assessments. Consequently, the operational
research community must investigate
new simulation techniques for predictive decision support when deciding
when or if to initiate a new analysis.
Data will no longer reside in standard
relational databases, but in more distributed data stores spanning users
of a larger network. As such, new
comprehensive cross-network, crosscloud data models must be developed
that are designed to optimize performance based on the distribution
of information and users. Finally,
conventional security and access control systems, such as the active directory,
are based on the tree-structured organization of users. In a socially connected
world, however, these policies must
leverage interconnected, graph-based
social relationships. A need will exist for
highly self-configurable security policies
to protect usersâ€™ security and privacy
while also preserving privacy embedded
within the data. These and other techniques will significantly enhance and
extend the information age.
1. â€œThe Data Deluge: Businesses, Governments and Society Are Only Starting to
Tap Its Vast Potential,â€ The Economist, 25
Feb. 2010; www.economist.com/opinion/
2. V. Gewin, â€œThe New Networking
Nexus,â€ Nature, vol. 451, no. 7181, 2008,
3. Y. Wei and M.B. Blake, â€œService-Oriented
Computing and Cloud Computing: Challenges and Opportunities,â€ IEEE Internet
Computing, vol. 14, no. 6, 2010, pp. 72â€“76.
4. A. Mislove et al., â€œMeasurement and
Analysis of Online Social Networks,â€
Proc. 7th ACM SIGCOMM Conf. Internet
Measurement, ACM, 2007, pp. 29â€“42.
5. P.J. Carrington, J. Scott, and S. Wasserman,
Models and Methods in Social Network
Analysis, Cambridge Univ. Press, 2005.
6. S. Boccaletti et al., â€œComplex Networks:
Structure and Dynamics,â€ Physics
Reports, Feb. 2006, pp. 175â€“308.
7. I.Z. Paul, C. Eaton, and P. Zikopoulos,
Understanding Big Data: Analytics for
Enterprise Class Hadoop and Streaming
Data, McGraw Hill Professional, 2011.
8. M. Stonebraker et al., â€œMapReduce and
Parallel DBMSs: Friends or Foes?â€ Comm.
ACM, vol. 53, no. 1, 2010, pp. 64â€“71.
9. S. Chaudhuri, U. Dayal, and V. Narasayya, â€œAn Overview of Business Intelligence Technology,â€ Comm. ACM, vol. 54,
no. 8, Aug. 2011, pp. 88â€“98.
10. S. Chaudhuri, â€œWhat Next? A Half-Dozen
Data Management Research Goals for Big
Data and the Cloud,â€ Proc. 31st Symp.
Principles of Database Systems, ACM,
2012, pp. 1â€“4.
Figure 4. A word cloud for recent cloud computing and big data research.
Resource management and performance issues are gaining the research
Social-Network-Sourced Big Data Analytics
SEPTEMBER/OCTOBER 2013 69
11. M. Stonebraker, â€œNew Opportunities for
New SQL,â€ Comm. ACM, vol. 55, no. 11,
2012, pp. 10â€“11.
12. N.B. Ellison, â€œSocial Network Sites: Definition, History, and Scholarship,â€ J. Computer-Mediated Communication, vol. 13,
no. 1, 2007, pp. 210â€“230.
13. C. Haythornthwaite, â€œSocial Networks
and Internet Connectivity Effects,â€ Information, Communication & Society, vol. 8,
no. 2, 2005, pp. 125â€“147.
14. A.L. Beberg and V.S. Pande, â€œStorage@
home: Petascale Distributed Storage,â€
Proc. Parallel and Distributed Processing
Symp., IEEE CS, 2007, pp. 1â€“6.
15. D.P. Anderson, â€œBoinc: A System for Public-Resource Computing and Storage,â€
Proc. 5th IEEE/ACM Intâ€™l Workshop Grid
Computing, IEEE CS, 2004, pp. 4â€“10.
16. B. Carminati et al., â€œA Semantic Web
Based Framework for Social Network
Access Control,â€ Proc. 14th ACM Symp.
Access Control Models and Technologies,
ACM, 2009, pp. 177â€“186.
17. B. Ali, W. Villegas, and M. Maheswaran,
â€œA Trust Based Approach for Protecting User Data in Social Networks,â€ Proc.
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on Collaborative Research, IBM, 2007,
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2002, pp. 36â€“41.
20. W. Tan and M.C. Zhou, Business and Scientific Workflows: A Web Service-Oriented Approach, Wiley-IEEE Press, 2013.
21. G. Klimeck et al., â€œnanoHUB.org:
Advancing Education and Research in
Nanotechnology,â€ Computing in Science
& Eng., vol. 10, no. 5, 2008, pp. 17â€“23.
Wei Tan is a research staff member at IBM
T.J. Watson Research Center. His research
interests include big data, cloud computing, service-oriented architecture, business and scientific workflows, and Petri
nets. Tan has a PhD in automation engineering from Tsinghua University, China.
Contact him at [email protected].
M. Brian Blake is a professor of computer
science and concurrent professor of
electrical and computer engineering,
and human genetics at the University
of Miami. His research interests include
service-oriented computing, workflow
systems, and software engineering. Blake
has a PhD in information and software
engineering from George Mason University. Heâ€™s a senior member of IEEE and
an ACM Distinguished Scientist. Contact
him at [email protected].
Iman Saleh is an assistant scientist at the University of Miami. Her research interests include
data modeling, Web services, formal methods, big data, and cryptography. Saleh has a
PhD in software engineering from Virginia
Tech. Sheâ€™s a member of ACM, IEEE, and the
Upsilon Pi Epsilon Honor Society for Computer Science at Virginia Tech. Contact her
at [email protected].
Schahram Dustdar is a full professor of computer science and head of the Distributed
Systems Group, Institute of Information Systems, at the Vienna University
of Technology. His research interests
include service-oriented architectures
and computing, cloud and elastic computing, complex and adaptive systems,
and context-aware computing. Dustdar is
an ACM Distinguished Scientist and IBM
Faculty Award recipient. Contact him at
Selected CS articles and columns
are also available for free at http://
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