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Unstructured meshes are characterized by data points irregularly distributed in the Euclidian space. Due to the irregular nature of these data, computing connectivity information between the mesh elements requires much more time and memory…

Driven by the increasing demand for low-latency and real-time processing, machine learning applications are steadily migrating toward edge computing platforms, where Field-Programmable Gate Arrays (FPGAs) are widely adopted for their energy…

Storage and memory systems for modern data analytics are heavily layered, managing shared persistent data, cached data, and non-shared execution data in separate systems such as distributed file system like HDFS, in-memory file system like…

The world needs diverse and unbiased data to train deep learning models. Currently data comes from a variety of sources that are unmoderated to a large extent. The outcomes of training neural networks with unverified data yields biased…

Run time packing is a common approach malware use to obfuscate their payloads, and automatic unpacking is, therefore, highly relevant. The problem has received much attention, and so far, solutions based on dynamic analysis have been the…

We design and analyze a mechanism for forming coalitions of peers in a data swarming system where peers have heterogeneous upload capacities. A coalition is a set of peers that explicitly cooperate with other peers inside the coalition via…

The data mining field is an important source of large-scale applications and datasets which are getting more and more common. In this paper, we present grid-based approaches for two basic data mining applications, and a performance…

Data replication and deployment of local SPARQL endpoints improve scalability and availability of public SPARQL endpoints, making the consumption of Linked Data a reality. This solution requires synchronization and specific query processing…

There have been intense research interests in moving object indexing in the past decade. However, existing work did not exploit the important property of skewed velocity distributions. In many real world scenarios, objects travel…

This paper introduces DAta-centric Peer-to-peer filE Sharing (DAPES), a data sharing protocol for scenarios with intermittent connectivity and user mobility. DAPES provides a set of semantically meaningful hierarchical naming abstractions…

Data center schedulers operate at unprecedented scales today to accommodate the growing demand for computing and storage power. The challenge that schedulers face is meeting the requirements of scheduling speeds despite the scale. To do so,…

Ubiquitous wearable and mobile devices provide access to a diverse set of data. However, the mobility demand for our devices naturally imposes constraints on their computational and communication capabilities. A solution is to locally learn…

Non-blocking search data structures offer scalability with a progress guarantee on high-performance multi-core architectures. In the recent past, "learned queries" have gained remarkable attention. It refers to predicting the rank of a key…

This paper introduces Mayfly, a federated analytics approach enabling aggregate queries over ephemeral on-device data streams without central persistence of sensitive user data. Mayfly minimizes data via on-device windowing and contribution…

Data management systems have traditionally been designed to support either long-running analytics queries or short-lived transactions, but an increasing number of applications need both. For example, online games, socio-mobile apps, and…

Big Data today is being generated at an unprecedented rate from various sources such as sensors, applications, and devices, and it often needs to be enriched based on other reference information to support complex analytical queries.…

We show how faceted search using a combination of traditional classification systems and mixed-membership topic models can go beyond keyword search to inform resource discovery, hypothesis formulation, and argument extraction for…

The growing pressure on cloud application scalability has accentuated storage performance as a critical bottle- neck. Although cache replacement algorithms have been extensively studied, cache prefetching - reducing latency by retrieving…

This study proposes a novel storage engine, SynchroStore, designed to address the inefficiency of update operations in columnar storage systems based on Log-Structured Merge Trees (LSM-Trees) under hybrid workload scenarios. While columnar…

Apache Kafka addresses the general problem of delivering extreme high volume event data to diverse consumers via a publish-subscribe messaging system. It uses partitions to scale a topic across many brokers for producers to write data in…