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Powerful abstractions such as dataframes are only as efficient as their underlying runtime system. The de-facto distributed data processing framework, Apache Spark, is poorly suited for the modern cloud-based data-science workloads due to…
Hadoop and Spark are widely used distributed processing frameworks for large-scale data processing in an efficient and fault-tolerant manner on private or public clouds. These big-data processing systems are extensively used by many…
As declarative query processing techniques expand in scope --- to the Web, data streams, network routers, and cloud platforms --- there is an increasing need for adaptive query processing techniques that can re-plan in the presence of…
As the size of modern data sets exceeds the disk and memory capacities of a single computer, machine learning practitioners have resorted to parallel and distributed computing. Given that optimization is one of the pillars of machine…
The query optimizer is a fundamental component of database management systems. Recent studies have shown that learned query optimizers outperform traditional cost-based query optimizers. However, they fail to exploit valuable runtime…
Spatial crowdsourcing (SC) engages large worker pools for location-based tasks, attracting growing research interest. However, prior SC task allocation approaches exhibit limitations in computational efficiency, balanced matching, and…
Very large volumes of spatial data increasingly become available and demand effective management. While there has been decades of research on spatial data management, few works consider the current state of commodity hardware, having…
Data processing systems offer an ever increasing degree of parallelism on the levels of cores, CPUs, and processing nodes. Query optimization must exploit high degrees of parallelism in order not to gradually become the bottleneck of query…
Data analytic applications built upon big data processing frameworks such as Apache Spark are an important class of applications. Many of these applications are not latency-sensitive and thus can run as batch jobs in data centers. By…
We introduce BriskStream, an in-memory data stream processing system (DSPSs) specifically designed for modern shared-memory multicore architectures. BriskStream's key contribution is an execution plan optimization paradigm, namely RLAS,…
Analytic queries enable sophisticated large-scale data analysis within many commercial, scientific and medical domains today. Data skew is a ubiquitous feature of these real-world domains. In a retail database, some products are typically…
Stochastic algorithms are efficient approaches to solving machine learning and optimization problems. In this paper, we propose a general framework called Splash for parallelizing stochastic algorithms on multi-node distributed systems.…
When data stores and users are distributed geographically, it is essential to organize distributed data cache points at ideal locations to minimize data transfers. To answer this, we are developing an adaptive distributed data caching…
The essence of distributed computing systems is how to schedule incoming requests and how to allocate all computing nodes to minimize both time and computation costs. In this paper, we propose a cost-aware optimal scheduling and allocation…
Today's data centers have an abundance of computing resources, hosting server clusters consisting of as many as tens or hundreds of thousands of machines. To execute a complex computing task over a data center, it is natural to distribute…
Modern large-scale scientific applications consist of thousands to millions of individual tasks. These tasks involve not only computation but also communication with one another. Typically, the communication pattern between tasks is sparse…
Multi-criteria decision making has been made possible with the advent of skyline queries. However, processing such queries for high dimensional datasets remains a time consuming task. Real-time applications are thus infeasible, especially…
With the widespread use of shared-nothing clusters of servers, there has been a proliferation of distributed object stores that offer high availability, reliability and enhanced performance for MapReduce-style workloads. However, relational…
Modern applications increasingly rely on inference serving systems to provide low-latency insights with a diverse set of machine learning models. Existing systems often utilize resource elasticity to scale with demand. However, many…
Distributed cloud environments hosting data-intensive applications often experience slowdowns due to network congestion, asymmetric bandwidth, and inter-node data shuffling. These factors are typically not captured by traditional host-level…