... two notable
projects in this direction.
3.4 Data Distribution
The conventional wisdom for large- scale databases is to always
send the computation to the data, rather than the other way around.
In ... release of DBMS-X, a parallel SQL
DBMS from a major relational database vendor that stores data in
A Comparison of Approaches to Large- ScaleData Analysis
Andrew Pavlo Erik Paulson Alexander Rasin
Brown ... since analysis tasks on large
data sets are often I/O bound, trading CPU cycles (needed to de-
compress input data) for I/O bandwidth (compressed data means
that there is less data to read) is a good...
... 4-2 Large- Scale MPLS VPN Deployment Copyright 2000, Cisco Systems, Inc.
MP-BGP Scalability Mechanisms ... discarded by the
receiving router, prior to sending the information to the receiving router.
4-4 Large- Scale MPLS VPN Deployment Copyright 2000, Cisco Systems, Inc.
Partitioned Route Reflectors ... Questions
n Describe BGP scaling issues in a MPLS VPN network.
The number of routes in a very large MPLS/VPN network may result in
exceeding the resources of the PE routers.
MPLS VPN uses...
... 4
Large- Scale MPLS VPN
Deployment
4-2 Large- Scale MPLS VPN Deployment Copyright 2000, Cisco Systems, Inc.
MP-BGP Scalability ... updates by uploading a
filter to the neighbor.
n Why are outbound route filters useful?
4-4 Large- Scale MPLS VPN Deployment Copyright 2000, Cisco Systems, Inc.
Partitioned Route Reflectors ... Questions
n Describe BGP scaling issues in a MPLS VPN network.
The number of routes in a very large MPLS/VPN network may result in
exceeding the resources of the PE routers.
MPLS VPN uses...
... technological changes. Google is designed to
scale well to extremely largedata sets. It makes efficient use of storage space to store the index. Its data
structures are optimized for fast and ... over
them.
6.1 Future Work
A large- scale web search engine is a complex system and much remains to be done. Our immediate
goals are to improve search efficiency and to scale to approximately 100 ... design goal was to build an architecture that can support novel research activities on
large- scale web data. To support novel research uses, Google stores all of the actual documents it crawls
in...
... Conditions for Large-
Scale Impulsive Dynamical Systems 249
10.5 Specialization to Large- Scale Linear Impulsive Dynamical
Systems 259
10.6 Stability of Feedback Interconnections of Large- Scale Im-
pulsive ... thermodynamic large- scale system necessarily
leads to nonconservation of ectropy and entropy. In addition, using the sys-
tem ectropy as a Lyapunov function candidate, we show that our large- scale
thermodynamic ... Introduction 305
13.2 Hybrid Decentralized Control and Large- Scale Impulsive
Dynamical Systems 306
13.3 Hybrid Decentralized Control for Large- Scale
Dynamical Systems 313
13.4 Interconnected Euler-Lagrange...
... Erin Duggan
May 31, 2012 212-335-9400
DA VANCE: ABACUS BANK AND 19 INDIVIDUALS CHARGED IN
LARGE- SCALE MORTGAGE FRAUD CONSPIRACY
Employees and Managers Charged With Routinely Submitting False...
... presents Scribe, a large- scale event notification infrastruc-
ture for topic-based publish-subscribe applications. Scribe supports large num-
bers of topics, with a potentially large number of subscribers ... network can scale to an extremely large number of nodes
because the algorithms to build and maintain the network have space and time costs of
. This enables support for extremely large groups ... a large- scale and fully decentralized event notification sys-
tem built on top of Pastry, a peer-to-peer object location and routing substrate overlayed
on the Internet. Scribe is designed to scale...
... promise of large-
scale discriminative training for SMT is to scale to
arbitrary types and numbers of features and to pro-
vide sufficient statistical support by parameter esti-
mation on large sample ... 2005):
∇l
j
(w) =
−¯x
j
if w, ¯x
j
≤ 0,
0 else.
Our baseline algorithm 1 (SDG) scales pairwise
ranking to largescale scenarios. The algorithm takes
an average over the final weight updates of ... Europarl data beyond
one epoch because features vectors grew too large to
be kept in memory.
6 Discussion
We presented an approach to scaling discrimina-
tive learning for SMT not only to large...
... moderate amounts of data (Chelba, 1997; Xu
et al., 2002; Charniak, 2001; Hall, 2004; Roark,
2004), these models have only recently been scaled
to the impressive amounts of data routinely used ... binary classification accuracy.
each training sentence, we can very easily scale
our model to much larger amounts of data. In Ta-
ble 4, we also show the performance of the gener-
ative models ... n-gram case, we would like to pick h
to be large enough to capture relevant dependencies,
but small enough that we can obtain meaningful es-
timates from data. We start with a straightforward
choice...
... 700,000 words annotated in the LDC data
and also for the points in the new data that we ac-
quired via MTurk for $205.80 USD. We find that
the slope fit to our new data is 6.6245E-06 BLEU
points per ... on giant corpora many orders of mag-
nitude larger than previously used, they do lay out
how AL might be useful to apply to acquire data
to augment a large set cheaply because they rec-
ognize ... ex-
tremely small set of seed data. Also, by SMT stan-
dards, they only add a very tiny amount of data
during AL. All their simulations top out at 10,000
sentences of labeled data and the models learned
have...
... first work of building a
complex largescale distributed language model with
a principled approach that is more powerful than n-
grams when both trained on a very large corpus with
up to a billion ... very large corpora to train our compos-
ite language model, both the data and the parameters
can’t be stored in a single machine, so we have to
resort to distributed computing. The topic of large
scale ... Simpli-
fied data processing on large clusters. Operating Sys-
tems Design and Implementation (OSDI), 137-150.
A. Dempster, N. Laird and D. Rubin. 1977. Maximum
likelihood estimation from incomplete data...
... en-
tries from a large amount of dependency relations
in Web documents. To our knowledge, no one else
has performed this type of clustering on such a large
scale. Wikipedia also produced a large gazetteer
of ... clustering with a vocabulary
that is large enough to cover the many named entities
required to improve the accuracy of NER is difficult.
We enabled such large- scale clustering by paralleliz-
ing ... expectation-
maximization (EM) and thus enabled the con-
struction of large- scale MN clusters. We
demonstrated with the IREX dataset for the
Japanese NER that using the constructed clus-
ters as...
... many identical
sentences. We address this weakness of co-selection
Evaluation challenges in large- scale document summarization
Dragomir R. Radev
U. of Michigan
radev@umich.edu
Wai Lam
Chinese ... Michigan
hqi@umich.edu
Elliott Drabek
Johns Hopkins U.
edrabek@cs.jhu.edu
Abstract
We present a large- scale meta evaluation
of eight evaluation measures for both
single-document and multi-document
summarizers. ... summa-
rization in a standard and inexpensive way is a diffi-
cult task (Mani et al., 2001). Traditional large- scale
evaluations are either too simplistic (using measures
like precision, recall, and percent...
... integrated
these largescale linguistic resources into our
natural language understanding system. Client-
server architecture was used to make a large
volume of lexical information and a large
knowledge ... understanding systems.
For this reason, large- scale linguistic resources
have been compiled and made available by
organizations such as the Linguistic Data
Consortium (Comlex) and Princeton ...
WordNet.bas ed
KB
Figure 2. Integration of KB Server data with core KB
(WordNet-based KB concept names from ISI see text)
984
Integration of Large- Scale Linguistic Resources in a Natural
Language...