6,879 research outputs found
Cache Serializability: Reducing Inconsistency in Edge Transactions
Read-only caches are widely used in cloud infrastructures to reduce access
latency and load on backend databases. Operators view coherent caches as
impractical at genuinely large scale and many client-facing caches are updated
in an asynchronous manner with best-effort pipelines. Existing solutions that
support cache consistency are inapplicable to this scenario since they require
a round trip to the database on every cache transaction.
Existing incoherent cache technologies are oblivious to transactional data
access, even if the backend database supports transactions. We propose T-Cache,
a novel caching policy for read-only transactions in which inconsistency is
tolerable (won't cause safety violations) but undesirable (has a cost). T-Cache
improves cache consistency despite asynchronous and unreliable communication
between the cache and the database. We define cache-serializability, a variant
of serializability that is suitable for incoherent caches, and prove that with
unbounded resources T-Cache implements this new specification. With limited
resources, T-Cache allows the system manager to choose a trade-off between
performance and consistency.
Our evaluation shows that T-Cache detects many inconsistencies with only
nominal overhead. We use synthetic workloads to demonstrate the efficacy of
T-Cache when data accesses are clustered and its adaptive reaction to workload
changes. With workloads based on the real-world topologies, T-Cache detects
43-70% of the inconsistencies and increases the rate of consistent transactions
by 33-58%.Comment: Ittay Eyal, Ken Birman, Robbert van Renesse, "Cache Serializability:
Reducing Inconsistency in Edge Transactions," Distributed Computing Systems
(ICDCS), IEEE 35th International Conference on, June~29 2015--July~2 201
EXPLAINABLE FEATURE DRIFT MONITORING SYSTEM FOR PREDICTIVE MACHINE LEARNING MODELS
The present disclosure provides a novel method wherein a classifier-based drift scoring model is initially constructed using historical data. The derivation of SHAP (SHapley Additive exPlanations) values from the drift scoring model is employed to discern the individual contributions of various features to the calculated drift score. To enhance the granularity of drift pattern comprehension, new data is systematically categorized into multiple clusters based on the similarity of their drift patterns, as elucidated by SHAP values. The performance degradation of a deployed predictive model is then meticulously evaluated under distinct drift patterns. This evaluation offers detailed insights into the differential impacts of feature drift on the predictive model. Decisions regarding the necessity of updating a deployed model are made by considering both population-level and cluster-level performance degradation estimations. This dual assessment ensures a comprehensive understanding of the global impact as well as the specific effects within designated drift pattern clusters. A systematic pipeline is proposed for the continual update of the drift monitoring system over time. This pipeline ensures the perpetual relevance of the measured drift patterns and the estimations of model degradation, aligning them with the evolving data landscape. Regular updates to the drift monitoring system maintain the accuracy and efficacy of decision-making processes concerning model updates
Automatic Schema Design for Co-Clustered Tables
Schema design of analytical workloads provides opportunities to index, cluster, partition and/or materialize. With these opportunities also the complexity of finding the right setup rises. In this paper we present an automatic schema design approach for a table co-clustering scheme called Bitwise Dimensional Co-Clustering, aimed at schemas with a moderate amount dimensions, but not limited to typical star and snowflake schemas. The goal is to design one primary schema and keep the knobs to turn to a minimum while providing a robust schema for a wide range of queries. In our approach a clustered schema is derived by trying to apply dimensions throughout the whole schema and co-cluster as many tables as possible according to at least one common dimension. Our approach is based on the assumption that initially foreign key relationships and a set of dimensions are defined based on classic DDL
Constraint-Based Ontology Induction From Online Customer Reviews
We present an unsupervised, domain-independent technique for inducing a product-specific ontology of product features based upon online customer reviews. We frame ontology induction as a logical assignment problem and solve it with a bounds consistency constrained logic program. Using shallow natural language processing techniques, reviews are parsed into phrase sequences where each phrase refers to a single concept. Traditional document clustering techniques are adapted to collect phrases into initial concepts. We generate a token graph for each initial concept cluster and find a maximal clique to define the corresponding logical set of concept sub-elements. The logic program assigns tokens to clique sub-elements. We apply the technique to several thousand digital camera customer reviews and evaluate the results by comparing them to the ontologies represented by several prominent online buying guides. Because our results are drawn directly from customer comments, differences between our automatically induced product features and those in extant guides may reflect opportunities for better managing customer-producer relationships rather than errors in the process
Storage Solutions for Big Data Systems: A Qualitative Study and Comparison
Big data systems development is full of challenges in view of the variety of
application areas and domains that this technology promises to serve.
Typically, fundamental design decisions involved in big data systems design
include choosing appropriate storage and computing infrastructures. In this age
of heterogeneous systems that integrate different technologies for optimized
solution to a specific real world problem, big data system are not an exception
to any such rule. As far as the storage aspect of any big data system is
concerned, the primary facet in this regard is a storage infrastructure and
NoSQL seems to be the right technology that fulfills its requirements. However,
every big data application has variable data characteristics and thus, the
corresponding data fits into a different data model. This paper presents
feature and use case analysis and comparison of the four main data models
namely document oriented, key value, graph and wide column. Moreover, a feature
analysis of 80 NoSQL solutions has been provided, elaborating on the criteria
and points that a developer must consider while making a possible choice.
Typically, big data storage needs to communicate with the execution engine and
other processing and visualization technologies to create a comprehensive
solution. This brings forth second facet of big data storage, big data file
formats, into picture. The second half of the research paper compares the
advantages, shortcomings and possible use cases of available big data file
formats for Hadoop, which is the foundation for most big data computing
technologies. Decentralized storage and blockchain are seen as the next
generation of big data storage and its challenges and future prospects have
also been discussed
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