1. CONTINUOUS TOP-K MONITORING ON DOCUMENT STREAMS
Abstract: The efficient processing of document streams plays an important
role in many information filtering systems. Emerging applications, such as news
update filtering and social network notifications, demand presenting end-users
with the most relevant content to their preferences. In this work, user
preferences are indicated by a set of keywords. A central server monitors the
document stream and continuously reports to each user the top-k documents that
are most relevant to her keywords. Our objective is to support large numbers of
users and high stream rates, while refreshing the top-k results almost instantaneously.
Our solution abandons the traditional frequency-ordered indexing approach.
Instead, it follows an identifier-ordering paradigm that suits better the
nature of the problem. When complemented with a novel, locally adaptive
technique, our method offers (i) proven optimality the number of considered queries per stream
event, and (ii) an order of magnitude shorter response time (i.e., time to
refresh the query results) than the current state-of-the-art.
2.
PREDICTING STUDENT
ACTIONS IN A PROCEDURAL TRAINING ENVIRONMENT
Abstract: Data mining is known to have a potential for predicting user performance.
However, there are few studies that explore its potential for predicting
student behavior in a procedural training environment. This paper presents a
collective student model, which is built from past student logs. These logs are
firstly grouped into clusters. Then an extended automaton is created for each
cluster based on the sequences of events found in the cluster logs. The main
objective of this model is to predict the actions of new students for improving
the tutoring feedback provided by an intelligent tutoring system. The proposed
model has been validated using student logs collected in a 3D virtual
laboratory for teaching biotechnology. As a result of this validation, we concluded
that the model can provide reasonably good predictions and can support tutoring
feedback that is better adapted to each student type.
3.
DYNAMIC FACET ORDERING FOR FACETED PRODUCT SEARCH ENGINES
Abstract:
Faceted browsing is widely used in Web shops
and product comparison sites. In these cases, a fixed ordered list of facets is
often employed. This approach suffers from two main issues. First, one needs to
invest a significant amount of time to devise an effective list. Second, with a
fixed list of facets it can happen that a facet becomes useless if all products
that match the query are associated to that particular facet. In this work, we
present a framework for dynamic facet ordering in e-commerce. Based on measures
for specificity and dispersion of facet values, the fully automated algorithm
ranks those properties and facets on top that lead to a quick drill-down for
any possible target product. In contrast to existing solutions, the framework
addresses e-commerce specific aspects, such as the possibility of multiple
clicks, the grouping of facets by their corresponding properties, and the
abundance of numeric facets. In a large-scale simulation and user study, our
approach was, in general, favorably compared to a facet list created by domain
experts, a greedy approach as baseline, and a state-of-the-art entropy-based
solution.
4.
TEMPORAL TASK SCHEDULING
WITH CONSTRAINED SERVICE DELAY FOR PROFIT MAXIMIZATION IN HYBRID CLOUDS
Abstract: As cloud computing is becoming growingly popular, consumers’ tasks
around the world arrive in cloud data centers. A private cloud provider aims to
achieve profit maximization by intelligently scheduling tasks while
guaranteeing the service delay bound of delay-tolerant tasks. However, the
aperiodicity of arrival tasks brings a challenging problem of how to
dynamically schedule all arrival tasks given the fact that the capacity of a
private cloud provider is limited. Previous works usually provide an admission
control to intelligently refuse some of arrival tasks. Nevertheless, this will
decrease the throughput of a private cloud, and cause revenue loss. This paper
studies the problem of how to maximize the profit of a private cloud in hybrid
clouds while guaranteeing the service delay bound of delay-tolerant tasks. We
propose a profit maximization algorithm (PMA) to discover the temporal
variation of prices in hybrid clouds. The temporal task scheduling provided by
PMA can dynamically schedule all arrival tasks to execute in private and public
clouds. The sub problem in each iteration of PMA is solved by the proposed
hybrid heuristic optimization algorithm, simulated annealing particle swarm
optimization (SAPSO). Besides, SAPSO is compared with existing baseline
algorithms. Extensive simulation experiments demonstrate that the proposed
method can greatly increase the throughput and the profit of a private cloud
while guaranteeing the service delay bound.
5.
AN ITERATIVE
CLASSIFICATION SCHEME FOR SANITIZING LARGE-SCALE DATASETS
Abstract: Cheap ubiquitous computing enables the collection of massive
amounts of personal data in a wide variety of domains. Many organizations aim
to share such data while obscuring features that could disclose personally
identifiable information. Much of this data exhibits weak structure (e.g.,
text), such that machine learning approaches have been developed to detect and
remove identifiers from it. While learning is never perfect, and relying on
such approaches to sanitize data can leak sensitive information, a small risk
is often acceptable. Our goal is to balance the value of published data and the
risk of an adversary discovering leaked identifiers. We model data sanitization
as a game between 1) a publisher who chooses a set of classifiers to apply to
data and publishes only instances predicted as non-sensitive and 2) an attacker
who combines machine learning and manual inspection to uncover leaked
identifying information. We introduce a fast iterative greedy algorithm for the
publisher that ensures a low utility for a resource-limited adversary.
Moreover, using five text data sets we illustrate that our algorithm leaves
virtually no automatically identifiable sensitive instances for a
state-of-the-art learning algorithm, while sharing over 93% of the original data,
and completes after at most 5 iterations.
6.
COMPUTING SEMANTIC
SIMILARITY OF CONCEPTS IN KNOWLEDGE GRAPHS
Abstract: This paper presents a method for measuring the semantic similarity
between concepts in Knowledge Graphs (KGs) such as WordNet and DBpedia.
Previous work on semantic similarity methods have focused on either the
structure of the semantic network between concepts (e.g. path length and
depth), or only on the Information Content (IC) of concepts. We propose a
semantic similarity method, namely wpath, to combine these two approaches,
using IC to weight the shortest path length between concepts. Conventional corpus-based
IC is computed from the distributions of concepts over textual corpus, which is
required to prepare a domain corpus containing annotated concepts and has high
computational cost. As instances are already extracted from textual corpus and
annotated by concepts in KGs, graph-based IC is proposed to compute IC based on
the distributions of concepts over instances. Through experiments performed on
well known word similarity datasets, we show that the wpath semantic similarity
method has produced statistically significant improvement over other semantic
similarity methods. Moreover, in a real category classification evaluation, the
wpath method has shown the best performance in terms of accuracy and F score.
7.
INFLUENCE MAXIMIZATION IN
TRAJECTORY DATABASES
ABSTRACT:
In this paper, we
study a novel problem of influence maximization in trajectory databases that is
very useful in precise location-aware advertising. It finds k best trajectories
to be attached with a given advertisement and maximizes the expected influence
among a large group of audience. We show that the problem is NP-hard and
propose both exact and approximate solutions to find the best set of
trajectories. In the exact solution, we devise an expansion-based framework
that enumerates trajectory combinations in a best-first manner and propose
three types of upper bound estimation techniques to facilitate early
termination. In addition, we propose a novel trajectory index to reduce the
influence calculation cost. To support large k, we propose a greedy solution
with an approximation ratio of (1-1/e), whose performance is further optimized
by a new proposed cluster-based method. We also propose a threshold method that
can support any approximation ratio ∈ (0, 1]. In addition, we extend our problem to support the
scenario when there are a group of advertisements. In our experiments, we use
real datasets to construct user profiles, motion patterns and trajectory
databases. The experimental results verified the efficiency of our proposed
methods.
8.
INFLUENTIAL NODE TRACKING ON DYNAMIC SOCIAL NETWORK: AN INTERCHANGE
GREEDY APPROACH
Abstract: As both social network structure and strength of influence between
individuals evolve constantly, it requires to track the influential nodes under
a dynamic setting. To address this problem, we explore the Influential Node
Tracking (INT) problem as an extension to the traditional Influence
Maximization problem (IM) under dynamic social networks. While Influence
Maximization problem aims at identifying a set of k nodes to maximize the joint
influence under one static network, INT problem focuses on tracking a set of
influential nodes that keeps maximizing the influence as the network evolves.
Utilizing the smoothness of the evolution of the network structure, we propose
an efficient algorithm, Upper Bound Interchange Greedy (UBI) and a variant,
UBI+. Instead of constructing the seed set from the ground, we start from the
influential seed set we find previously and implement node replacement to
improve the influence coverage. Furthermore, by using a fast update method by
calculating the marginal gain of nodes, our algorithm can scale to dynamic
social networks with millions of nodes. Empirical experiments on three real
large-scale dynamic social networks show that our UBI and its variants, UBI+
achieves better performance in terms of both influence coverage and running
time.
9.
PERSONAL WEBREVISITATION
BY CONTEXT & CONTENT KEYWORD WITH RELEVANCE FEEDBACK
ABSTRACT:
Getting back to previously viewed web pages is a common yet uneasy
task for users due to the large volume of personally accessed information on
the web. This paper leverages human’s natural recall process of using episodic
and semantic memory cues to facilitate recall, and presents a personal web
revisitation technique called WebPagePrev
through context and content keywords. Underlying techniques for context and
content memories’ acquisition, storage, decay, and utilization for page
re-finding are discussed. A relevance feedback mechanism is also involved to
tailor to individual’s memory strength and revisitation habits. Our 6-month
user study shows that:
•
Compared with the existing web revisitation
tool Memento, History List Searching method, and Search Engine method,
the proposed WebPagePrev delivers
the best re-finding quality in finding rate (92.10%), average F1-measure
(0.4318) and average rank error (0.3145).
•
Our dynamic management of context and
content memories including decay and reinforcement strategy can mimic users’
retrieval and recall mechanism. With relevance feedback, the finding rate of WebPagePrev increases by 9.82%, average
F1-measure increases by 47.09%, and average rank error decreases by 19.44%
compared to stable memory management strategy.Among time, location, and
activity context factors in WebPagePrev,
activity is the best recall cue, and context+content based re-findingdelivers
the best performance, compared to context based re-finding and content based
re-finding.
10. PPRANK: ECONOMICALLY
SELECTING INITIAL USERS FOR INFLUENCE MAXIMIZATION IN SOCIAL NETWORKS.
Abstract: This paper focuses on seeking a new heuristic scheme for an
influence maximization problem in social networks: how to economically select a
subset of individuals (so-called seeds) to trigger a large cascade of further
adoptions of a new behavior based on a contagion process. Most existing works
on selection of seeds assumed that the constant number k seeds could be
selected, irrespective of the intrinsic property of each individual’s different
susceptibility of being influenced (e.g., it may be costly to persuade some
seeds to adopt a new behavior). In this paper, a price-performance-ratio
inspired heuristic scheme, PPRank, is proposed, which investigates how to
economically select seeds within a given budget and meanwhile try to maximize
the diffusion process. Our paper’s contributions are threefold. First, we
explicitly characterize each user with two distinct factors: the susceptibility
of being influenced (SI) and influential power (IP) representing the ability to
actively influence others and formulate users’ SIs and IPs according to their
social relations, and then, a convex price-demand curve-based model is utilized
to properly convert each user’s SI into persuasion cost (PC) representing the
cost used to successfully make the individual adopt a new behavior.
Furthermore, a novel cost-effective selection scheme is proposed, which adopts
both the price performance ratio (PC-IP ratio) and user’s IP as an integrated
selection criterion and meanwhile explicitly takes into account the overlapping
effect; finally, simulations using both artificially generated and real-trace
network data illustrate that, under the same budgets, PPRank can achieve larger
diffusion range than other heuristic and brute-force greedy schemes without
taking users’ persuasion costs into account.
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