146,411 research outputs found
LogBase: A Scalable Log-structured Database System in the Cloud
Numerous applications such as financial transactions (e.g., stock trading)
are write-heavy in nature. The shift from reads to writes in web applications
has also been accelerating in recent years. Write-ahead-logging is a common
approach for providing recovery capability while improving performance in most
storage systems. However, the separation of log and application data incurs
write overheads observed in write-heavy environments and hence adversely
affects the write throughput and recovery time in the system. In this paper, we
introduce LogBase - a scalable log-structured database system that adopts
log-only storage for removing the write bottleneck and supporting fast system
recovery. LogBase is designed to be dynamically deployed on commodity clusters
to take advantage of elastic scaling property of cloud environments. LogBase
provides in-memory multiversion indexes for supporting efficient access to data
maintained in the log. LogBase also supports transactions that bundle read and
write operations spanning across multiple records. We implemented the proposed
system and compared it with HBase and a disk-based log-structured
record-oriented system modeled after RAMCloud. The experimental results show
that LogBase is able to provide sustained write throughput, efficient data
access out of the cache, and effective system recovery.Comment: VLDB201
Encoding and Accessing Linguistic Representations in a Dynamically Structured Holographic Memory System
This paper presents a computational model that integrates a dynamically structured holographic memory system into the ACT-R cognitive architecture to explain how linguistic representations are encoded and accessed in memory. ACT-R currently serves as the most precise expression of the moment-by-moment working memory retrievals that support sentence comprehension. The ACT-R model of sentence comprehension is able to capture a range of linguistic phenomena, but there are cases where the model makes the wrong predictions, such as the over-prediction of retrieval interference effects during sentence comprehension. Here, we investigate one such case involving the processing of sentences with negative polarity items (NPIs) and consider how a dynamically structured holographic memory system might provide a cognitively plausible and principled explanation of some previously unexplained effects. Specifically, we show that by replacing ACT-R\u27s declarative memory with a dynamically structured memory, we can explain a wider range of behavioral data involving reading times and judgments of grammaticality. We show that our integrated model provides a better fit to human error rates and response latencies than the original ACT-R model. These results provide proof-of-concept for the unification of two independent computational cognitive frameworks
A Deep-structured Conditional Random Field Model for Object Silhouette Tracking
In this work, we introduce a deep-structured conditional random field
(DS-CRF) model for the purpose of state-based object silhouette tracking. The
proposed DS-CRF model consists of a series of state layers, where each state
layer spatially characterizes the object silhouette at a particular point in
time. The interactions between adjacent state layers are established by
inter-layer connectivity dynamically determined based on inter-frame optical
flow. By incorporate both spatial and temporal context in a dynamic fashion
within such a deep-structured probabilistic graphical model, the proposed
DS-CRF model allows us to develop a framework that can accurately and
efficiently track object silhouettes that can change greatly over time, as well
as under different situations such as occlusion and multiple targets within the
scene. Experiment results using video surveillance datasets containing
different scenarios such as occlusion and multiple targets showed that the
proposed DS-CRF approach provides strong object silhouette tracking performance
when compared to baseline methods such as mean-shift tracking, as well as
state-of-the-art methods such as context tracking and boosted particle
filtering.Comment: 17 page
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