329,680 research outputs found
Managing polyglot systems metadata with hypergraphs
A single type of data store can hardly fulfill every end-user requirements in the NoSQL world. Therefore, polyglot systems use different types of NoSQL datastores in combination. However, the heterogeneity of the data storage models makes managing the metadata a complex task in such systems, with only a handful of research carried out to address this. In this paper, we propose a hypergraph-based approach for representing the catalog of metadata in a polyglot system. Taking an existing common programming interface to NoSQL systems, we extend and formalize it as hypergraphs for managing metadata. Then, we define design constraints and query transformation rules for three representative data store types. Furthermore, we propose a simple query rewriting algorithm using the catalog itself for these data store types and provide a prototype implementation. Finally, we show the feasibility of our approach on a use case of an existing polyglot system.Peer ReviewedPostprint (author's final draft
IMPACT: Investigation of Mobile-user Patterns Across University Campuses using WLAN Trace Analysis
We conduct the most comprehensive study of WLAN traces to date. Measurements
collected from four major university campuses are analyzed with the aim of
developing fundamental understanding of realistic user behavior in wireless
networks. Both individual user and inter-node (group) behaviors are
investigated and two classes of metrics are devised to capture the underlying
structure of such behaviors.
For individual user behavior we observe distinct patterns in which most users
are 'on' for a small fraction of the time, the number of access points visited
is very small and the overall on-line user mobility is quite low. We clearly
identify categories of heavy and light users. In general, users exhibit high
degree of similarity over days and weeks.
For group behavior, we define metrics for encounter patterns and friendship.
Surprisingly, we find that a user, on average, encounters less than 6% of the
network user population within a month, and that encounter and friendship
relations are highly asymmetric. We establish that number of encounters follows
a biPareto distribution, while friendship indexes follow an exponential
distribution. We capture the encounter graph using a small world model, the
characteristics of which reach steady state after only one day.
We hope for our study to have a great impact on realistic modeling of network
usage and mobility patterns in wireless networks.Comment: 16 pages, 31 figure
PocketCare: Tracking the Flu with Mobile Phones using Partial Observations of Proximity and Symptoms
Mobile phones provide a powerful sensing platform that researchers may adopt
to understand proximity interactions among people and the diffusion, through
these interactions, of diseases, behaviors, and opinions. However, it remains a
challenge to track the proximity-based interactions of a whole community and
then model the social diffusion of diseases and behaviors starting from the
observations of a small fraction of the volunteer population. In this paper, we
propose a novel approach that tries to connect together these sparse
observations using a model of how individuals interact with each other and how
social interactions happen in terms of a sequence of proximity interactions. We
apply our approach to track the spreading of flu in the spatial-proximity
network of a 3000-people university campus by mobilizing 300 volunteers from
this population to monitor nearby mobile phones through Bluetooth scanning and
to daily report flu symptoms about and around them. Our aim is to predict the
likelihood for an individual to get flu based on how often her/his daily
routine intersects with those of the volunteers. Thus, we use the daily
routines of the volunteers to build a model of the volunteers as well as of the
non-volunteers. Our results show that we can predict flu infection two weeks
ahead of time with an average precision from 0.24 to 0.35 depending on the
amount of information. This precision is six to nine times higher than with a
random guess model. At the population level, we can predict infectious
population in a two-week window with an r-squared value of 0.95 (a random-guess
model obtains an r-squared value of 0.2). These results point to an innovative
approach for tracking individuals who have interacted with people showing
symptoms, allowing us to warn those in danger of infection and to inform health
researchers about the progression of contact-induced diseases
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