401,475 research outputs found
Faculty Recital: An Evening with Friends
Join us virtually for An Evening with Friends featuring performances by Judith Cole, Kennesaw State University Artist in Residence in Collaborative Piano. The program features faculty professors Helen Kim, Charae Krueger, Kenn Wagner, Nancy Conley, Doug Lindsey, Marc Miller, John Lawless, John Warren and former student Chani Maisonet performing several works never before heard on our concert hall stage. From Beethoven and Saint-Saens to Ewazen and music from a recently released film, this recital is designed for lovers of traditional concert music as well as those who enjoy lighter fare, all presented by a group of close friends who are School of Music faculty.https://digitalcommons.kennesaw.edu/musicprograms/2366/thumbnail.jp
Efficient Processing of k Nearest Neighbor Joins using MapReduce
k nearest neighbor join (kNN join), designed to find k nearest neighbors from
a dataset S for every object in another dataset R, is a primitive operation
widely adopted by many data mining applications. As a combination of the k
nearest neighbor query and the join operation, kNN join is an expensive
operation. Given the increasing volume of data, it is difficult to perform a
kNN join on a centralized machine efficiently. In this paper, we investigate
how to perform kNN join using MapReduce which is a well-accepted framework for
data-intensive applications over clusters of computers. In brief, the mappers
cluster objects into groups; the reducers perform the kNN join on each group of
objects separately. We design an effective mapping mechanism that exploits
pruning rules for distance filtering, and hence reduces both the shuffling and
computational costs. To reduce the shuffling cost, we propose two approximate
algorithms to minimize the number of replicas. Extensive experiments on our
in-house cluster demonstrate that our proposed methods are efficient, robust
and scalable.Comment: VLDB201
The Role of Proactive Adaptation in International Climate Change Mitigation Agreements
This paper investigates the role of proactive adaptation in international mitigation coalition formation. Adaptation is introduced into a three stage cartel game of coalition formation. We analytically derive the optimal level of mitigation and proactive adaptation for the singletons and coalition members. We introduce the AD-STACO model which is constructed based on the STACO model, which is an applied three-stage cartel formation model with 12 heterogenous regions
Pay One, Get Hundreds for Free: Reducing Cloud Costs through Shared Query Execution
Cloud-based data analysis is nowadays common practice because of the lower
system management overhead as well as the pay-as-you-go pricing model. The
pricing model, however, is not always suitable for query processing as heavy
use results in high costs. For example, in query-as-a-service systems, where
users are charged per processed byte, collections of queries accessing the same
data frequently can become expensive. The problem is compounded by the limited
options for the user to optimize query execution when using declarative
interfaces such as SQL. In this paper, we show how, without modifying existing
systems and without the involvement of the cloud provider, it is possible to
significantly reduce the overhead, and hence the cost, of query-as-a-service
systems. Our approach is based on query rewriting so that multiple concurrent
queries are combined into a single query. Our experiments show the aggregated
amount of work done by the shared execution is smaller than in a
query-at-a-time approach. Since queries are charged per byte processed, the
cost of executing a group of queries is often the same as executing a single
one of them. As an example, we demonstrate how the shared execution of the
TPC-H benchmark is up to 100x and 16x cheaper in Amazon Athena and Google
BigQuery than using a query-at-a-time approach while achieving a higher
throughput
The Revitalization of Mak Yong in the Malay World
Culture can only be revitalized when a cultural tradition is considered of significant importance by its owning community. How important it is for the identity of that community or in how far it symbolizes that community can only be established after in-depth study. It is crucial that revitalization activities involve the community. If not, efforts would be pointless. Mak Yong is one of the Malay performing art traditions that has been revitalized over the last five years. It is an art form that originates in southern Thailand and was brought to Bintan in the Riau Islands (Kepulauan Riau, Kepri) via Singapore. It combines dialogue, dance, singing, music, and stories and may still be found in the Riau Islands in Indonesia. It is interesting to show how Mak Yong has represented and expressed Malay dynamics by means of revitalizations efforts
VerdictDB: Universalizing Approximate Query Processing
Despite 25 years of research in academia, approximate query processing (AQP)
has had little industrial adoption. One of the major causes of this slow
adoption is the reluctance of traditional vendors to make radical changes to
their legacy codebases, and the preoccupation of newer vendors (e.g.,
SQL-on-Hadoop products) with implementing standard features. Additionally, the
few AQP engines that are available are each tied to a specific platform and
require users to completely abandon their existing databases---an unrealistic
expectation given the infancy of the AQP technology. Therefore, we argue that a
universal solution is needed: a database-agnostic approximation engine that
will widen the reach of this emerging technology across various platforms.
Our proposal, called VerdictDB, uses a middleware architecture that requires
no changes to the backend database, and thus, can work with all off-the-shelf
engines. Operating at the driver-level, VerdictDB intercepts analytical queries
issued to the database and rewrites them into another query that, if executed
by any standard relational engine, will yield sufficient information for
computing an approximate answer. VerdictDB uses the returned result set to
compute an approximate answer and error estimates, which are then passed on to
the user or application. However, lack of access to the query execution layer
introduces significant challenges in terms of generality, correctness, and
efficiency. This paper shows how VerdictDB overcomes these challenges and
delivers up to 171 speedup (18.45 on average) for a variety of
existing engines, such as Impala, Spark SQL, and Amazon Redshift, while
incurring less than 2.6% relative error. VerdictDB is open-sourced under Apache
License.Comment: Extended technical report of the paper that appeared in Proceedings
of the 2018 International Conference on Management of Data, pp. 1461-1476.
ACM, 201
Online Group-exercises for Older Adults of Different Physical Abilities
In this paper we describe the design and validation of a virtual fitness
environment aiming at keeping older adults physically and socially active. We
target particularly older adults who are socially more isolated, physically
less active, and with less chances of training in a gym. The virtual fitness
environment, namely Gymcentral, was designed to enable and motivate older
adults to follow personalised exercises from home, with a (heterogeneous) group
of remote friends and under the remote supervision of a Coach. We take the
training activity as an opportunity to create social interactions, by
complementing training features with social instruments. Finally, we report on
the feasibility and effectiveness of the virtual environment, as well as its
effects on the usage and social interactions, from an intervention study in
Trento, Ital
The Vadalog System: Datalog-based Reasoning for Knowledge Graphs
Over the past years, there has been a resurgence of Datalog-based systems in
the database community as well as in industry. In this context, it has been
recognized that to handle the complex knowl\-edge-based scenarios encountered
today, such as reasoning over large knowledge graphs, Datalog has to be
extended with features such as existential quantification. Yet, Datalog-based
reasoning in the presence of existential quantification is in general
undecidable. Many efforts have been made to define decidable fragments. Warded
Datalog+/- is a very promising one, as it captures PTIME complexity while
allowing ontological reasoning. Yet so far, no implementation of Warded
Datalog+/- was available. In this paper we present the Vadalog system, a
Datalog-based system for performing complex logic reasoning tasks, such as
those required in advanced knowledge graphs. The Vadalog system is Oxford's
contribution to the VADA research programme, a joint effort of the universities
of Oxford, Manchester and Edinburgh and around 20 industrial partners. As the
main contribution of this paper, we illustrate the first implementation of
Warded Datalog+/-, a high-performance Datalog+/- system utilizing an aggressive
termination control strategy. We also provide a comprehensive experimental
evaluation.Comment: Extended version of VLDB paper
<https://doi.org/10.14778/3213880.3213888
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