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Modern advanced analytics applications make use of machine learning techniques and contain multiple steps of domain-specific and general-purpose processing with high resource requirements. We present KeystoneML, a system that captures and optimizes the end-to-end large-scale machine learning applications for high-throughput training in a distributed environment with a high-level API. This approach...
Persistent storage is a fundamental abstraction in computing. It consists of a named set of data items that come into existence through explicit creation, persist through temporary failures of the system, until they are explicitly deleted. Sharing of data in distributed systems has become pervasive as these systems have grown in scale in terms of number of machines and the amount of data stored. The...
Data store replication results in a fundamental trade-off between operation latency and data consistency. At the weak end of the consistency spectrum is eventual consistency providing no limit to the staleness of data returned. However, anecdotally, eventual consistency is often “good enough” for practitioners given its latency and availability benefits. In this work, we explain why eventually consistent...
The problem of finding the edit distance between two sequences (and its closely related problem of longest common subsequence) are important problems with applications in many domains like virus scanners, security kernels, natural language translation and genome sequence alignment. The traditional dynamic-programming based algorithm is hard to parallelize on SIMD processors as the algorithm is memory...
Volatile memory forensic tools can extract valuable evidence from latent data structures present in memory dumps. However, current techniques are generally limited by a lack of understanding of the underlying data without the use of expert knowledge. In this paper, we characterize the nature of such evidence by using deep analysis techniques to better understand the life-cycle and recoverability of...
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