8 research outputs found
Mining User Interests from Social Media
Social media users readily share their preferences, life events, sentiment and opinions, and implicitly signal their thoughts, feelings, and psychological behavior. This makes social media a viable source of information to accurately and effectively mine users' interests with the hopes of enabling more effective user engagement, better quality delivery of appropriate services and higher user satisfaction. In this tutorial, we cover five important aspects related to the effective mining of user interests: (1) the foundations of social user interest modeling, such as information sources, various types of representation models and temporal features, (2) techniques that have been adopted or proposed for mining user interests, (3) different evaluation methodologies and benchmark datasets, (4) different applications that have been taking advantage of user interest mining from social media platforms, and (5) existing challenges, open research questions and exciting opportunities for further work
On the Feature Discovery for App Usage Prediction in Smartphones
With the increasing number of mobile Apps developed, they are now closely
integrated into daily life. In this paper, we develop a framework to predict
mobile Apps that are most likely to be used regarding the current device status
of a smartphone. Such an Apps usage prediction framework is a crucial
prerequisite for fast App launching, intelligent user experience, and power
management of smartphones. By analyzing real App usage log data, we discover
two kinds of features: The Explicit Feature (EF) from sensing readings of
built-in sensors, and the Implicit Feature (IF) from App usage relations. The
IF feature is derived by constructing the proposed App Usage Graph (abbreviated
as AUG) that models App usage transitions. In light of AUG, we are able to
discover usage relations among Apps. Since users may have different usage
behaviors on their smartphones, we further propose one personalized feature
selection algorithm. We explore minimum description length (MDL) from the
training data and select those features which need less length to describe the
training data. The personalized feature selection can successfully reduce the
log size and the prediction time. Finally, we adopt the kNN classification
model to predict Apps usage. Note that through the features selected by the
proposed personalized feature selection algorithm, we only need to keep these
features, which in turn reduces the prediction time and avoids the curse of
dimensionality when using the kNN classifier. We conduct a comprehensive
experimental study based on a real mobile App usage dataset. The results
demonstrate the effectiveness of the proposed framework and show the predictive
capability for App usage prediction.Comment: 10 pages, 17 figures, ICDM 2013 short pape
TOSI: a trust-oriented social influence evaluation method in contextual social networks
Online Social Networks (OSNs) have been used as the means for a variety of applications. For example, social networking platform has been used in employment system, e-Commerce and CRM system to improve the quality of recommendations with the assistance of social networks. In these applications, social influence acts as a significant role, affecting people's decision-making. However, the existing social influence evaluation methods do not fully consider the social contexts, i.e., the social relationships and the social trust between participants, and the preferences of participants, which have significant impact on social influence evaluation in OSNs. Thus, these existing methods cannot deliver accurate social influence evaluation results. In our paper, we propose a Trust-Oriented Social Influence evaluation method, called TOSI, with taking the social contexts into account. We conduct experiments onto two real social network datasets, i.e., Epinions and DBLP. The experimental results illustrate that our TOSI method greatly outperforms the state-of-the-art method SoCap in terms of effectiveness, efficiency and robustness
Exploration with Limited Memory: Streaming Algorithms for Coin Tossing, Noisy Comparisons, and Multi-Armed Bandits
Consider the following abstract coin tossing problem: Given a set of
coins with unknown biases, find the most biased coin using a minimal number of
coin tosses. This is a common abstraction of various exploration problems in
theoretical computer science and machine learning and has been studied
extensively over the years. In particular, algorithms with optimal sample
complexity (number of coin tosses) have been known for this problem for quite
some time.
Motivated by applications to processing massive datasets, we study the space
complexity of solving this problem with optimal number of coin tosses in the
streaming model. In this model, the coins are arriving one by one and the
algorithm is only allowed to store a limited number of coins at any point --
any coin not present in the memory is lost and can no longer be tossed or
compared to arriving coins. Prior algorithms for the coin tossing problem with
optimal sample complexity are based on iterative elimination of coins which
inherently require storing all the coins, leading to memory-inefficient
streaming algorithms.
We remedy this state-of-affairs by presenting a series of improved streaming
algorithms for this problem: we start with a simple algorithm which require
storing only coins and then iteratively refine it further and
further, leading to algorithms with memory,
memory, and finally a one that only stores a single extra coin in memory -- the
same exact space needed to just store the best coin throughout the stream.
Furthermore, we extend our algorithms to the problem of finding the most
biased coins as well as other exploration problems such as finding top-
elements using noisy comparisons or finding an -best arm in
stochastic multi-armed bandits, and obtain efficient streaming algorithms for
these problems
Elastic Dataflow Processing on the Cloud
Τα νεφη εχουν μετατραπει σε μια ελκυστικη πλατφορμα για την πολυπλοκη
επεξεργασια δεδομενων μεγαλης κλιμακας, ειδικα εξαιτιας της εννοιας της
ελαστικοτητας, η οποια και τα χαρακτηριζει: οι υπολογιστικοι ποροι
μπορουν να εκμισθωθουν δυναμικα και να χρησιμοποιουνται για οσο χρονο
ειναι απαραιτητο. Αυτο δινει την δυνατοτητα να δημιουργηθει μια εικονικη
υποδομη η οποια μπορει να αλλαζει δυναμικα στο χρονο. Οι συγχρονες
εφαρμογες απαιτουν την εκτελεση πολυπλοκων ερωτηματων σε Μεγαλα Δεδομενα
για την εξορυξη γνωσης και την υποστηριξη επιχειρησιακων αποφασεων. Τα
πολυπλοκα αυτα ερωτηματα, εκφραζονται σε γλωσσες υψηλου επιπεδου και
τυπικα μεταφραζονται σε ροες επεξεργασιας δεδομενων, η απλα ροες
δεδομενων. Ενα λογικο ερωτημα που τιθεται ειναι κατα ποσον η
ελαστικοτητα επηρεαζει την εκτελεση των ροων δεδομενων και με πιο τροπο.
Ειναι λογικο οτι η εκτελεση να ειναι πιθανον γρηγοροτερη αν
χρησιμοποιηθουν περισ- σοτεροι υπολογιστικοι ποροι, αλλα το κοστος θα
ειναι υψηλοτερο. Αυτο δημιουργει την εννοια της οικο-ελαστικοτητας, ενος
επιπλεον τυπου ελαστικοτητας ο οποιος προερχεται απο την οικονο- μικη
θεωρια, και συλλαμβανει τις εναλλακτικες μεταξυ του χρονου εκτελεσης και
του χρηματικου κοστους οπως προκυπτει απο την χρηση των πορων.
Στα πλαισια αυτης της διδακτορικης διατριβης, προσεγγιζουμε την
ελαστικοτητα με ενα ενοποιημενο μοντελο που περιλαμβανει και τις δυο
ειδων ελαστικοτητες που υπαρχουν στα υπολογιστικα νεφη. Αυτη η
ενοποιημενη προσεγγιση της ελαστικοτητας ειναι πολυ σημαντικη στην
σχεδιαση συστηματων που ρυθμιζονται αυτοματα (auto-tuned) σε περιβαλλοντα
νεφους. Αρχικα δειχνουμε οτι η οικο-ελαστικοτητα υπαρχει σε αρκετους
τυπους υπολογισμου που εμφανιζονται συχνα στην πραξη και οτι μπορει να
βρεθει χρησιμοποιωντας εναν απλο, αλλα ταυτοχρονα αποδοτικο και ε-
πεκτασιμο αλγοριθμο. Επειτα, παρουσιαζουμε δυο εφαρμογες που
χρησιμοποιουν αλγοριθμους οι οποιοι χρησιμοποιουν το ενοποιημενο μοντελο
ελαστικοτητας που προτεινουμε για να μπορουν να προσαρμοζουν δυναμικα το
συστημα στα ερωτηματα της εισοδου: 1) την ελαστικη επεξεργασια αναλυτικων
ερωτηματων τα οποια εχουν πλανα εκτελεσης με μορφη δεντρων με σκοπο την
μεγι- στοποιηση του κερδους και 2) την αυτοματη διαχειριση χρησιμων
ευρετηριων λαμβανοντας υποψη το χρηματικο κοστος των υπολογιστικων και
των αποθηκευτικων πορων. Τελος, παρουσιαζουμε το EXAREME, ενα συστημα για
την ελαστικη επεξεργασια μεγαλου ογκου δεδομενων στο νεφος το οποιο
εχει χρησιμοποιηθει και επεκταθει σε αυτην την δουλεια. Το συστημα
προσφερει δηλωτικες γλωσσες που βασιζονται στην SQL επεκταμενη με
συναρτησεις οι οποιες μπορει να οριστουν απο χρηστες (User-Defined
Functions, UDFs). Επιπλεον, το συντακτικο της γλωσσας εχει επεκταθει με
στοιχεια παραλληλισμου. Το EXAREME εχει σχεδιαστει για να εκμεταλλευεται
τις ελαστικοτη- τες που προσφερουν τα νεφη, δεσμευοντας και αποδεσμευοντας
υπολογιστικους πορους δυναμικα με σκοπο την προσαρμογη στα ερωτηματα.Clouds have become an attractive platform for the large-scale processing of
modern applications on Big Data, especially due to the concept of elasticity,
which characterizes them: resources can be leased on demand and used for as
much time as needed, offering the ability to create virtual infrastructures
that change dynamically over time. Such applications often require processing
of complex queries that are expressed in a high-level language and are
typically transformed into data processing flows (dataflows). A logical
question that arises is whether elasticity affects dataflow execution and in
which way. It seems reasonable that the execution is faster when more resources
are used, however the monetary cost is higher. This gives rise to the concept
eco-elasticity, an additional kind of elasticity that comes from economics, and
captures the trade-offs between the response time of the system and the amount
of money we pay for it as influenced by the use of different amounts of
resources.
In this thesis, we approach the elasticity of clouds in a unified way that
combines both the traditional notion and eco-elasticity. This unified
elasticity concept is essential for the development of auto-tuned systems in
cloud environments. First, we demonstrate that eco-elasticity exists in several
common tasks that appear in practice and that can be discovered using a simple,
yet highly scalable and efficient algorithm. Next, we present two cases of
auto-tuned algorithms that use the unified model of elasticity in order to
adapt to the query workload: 1) processing analytical queries in the form of
tree execution plans in order to maximize profit and 2) automated index
management taking into account compute and storage re- sources. Finally, we
describe EXAREME, a system for elastic data processing on the cloud that has
been used and extended in this work. The system offers declarative languages
that are based on SQL with user-defined functions (UDFs) extended with
parallelism primi- tives. EXAREME exploits both elasticities of clouds by
dynamically allocating and deallocating compute resources in order to adapt to
the query workload