Related papers: Near Data Processing in Taurus Database
Non-relational database systems (NRDS), such as graph, document, key-value, and wide-column, have gained much attention in various trending (business) application domains like smart logistics, social network analysis, and medical…
NoSQL databases have become an important component of many big data and real-time web applications. Their distributed nature and scalability make them an ideal data storage repository for a variety of use cases. While NoSQL databases are…
Multi-socket multi-core servers are used for solving some of the important problems in computing. Remote DRAM accesses can impact performance of certain applications running on such servers. This paper presents a new near linear operating…
Large scale cloud data analytics applications are often CPU bound. Most of these cycles are wasted: benchmarks written in C++ run 10-51 times faster than frameworks such as Naiad and Spark. However, calling faster implementations from those…
Near neighbor search (NNS) is a powerful abstraction for data access; however, data indexing is troublesome even for approximate indexes. For intrinsically high-dimensional data, high-quality fast searches demand either indexes with…
Relational database management system (RDBMS) is a major undergraduate course taught in many universities worldwide as part of their computer science program. A core component of such course is the design and implementation of the query…
In the era of cloud computing, privacy-preserving computation offloading is crucial for safeguarding sensitive data. Fully Homomorphic Encryption (FHE) enables secure processing of encrypted data, but the inherent computational complexity…
Neural architecture search (NAS) recently attracts much research attention because of its ability to identify better architectures than handcrafted ones. However, many NAS methods, which optimize the search process in a discrete search…
Deep learning models are increasingly used for end-user applications, supporting both novel features such as facial recognition, and traditional features, e.g. web search. To accommodate high inference throughput, it is common to host a…
We consider an auto-scaling technique in a cloud system where virtual machines hosted on a physical node are turned on and off depending on the queue's occupation (or thresholds), in order to minimise a global cost integrating both energy…
Shark is a new data analysis system that marries query processing with complex analytics on large clusters. It leverages a novel distributed memory abstraction to provide a unified engine that can run SQL queries and sophisticated analytics…
In this paper, we study CPU utilization time patterns of several Map-Reduce applications. After extracting running patterns of several applications, the patterns with their statistical information are saved in a reference database to be…
Near-data accelerators (NDAs) that are integrated with main memory have the potential for significant power and performance benefits. Fully realizing these benefits requires the large available memory capacity to be shared between the host…
In differentially private (DP) tabular data synthesis, the consensus is that statistical models are better than neural network (NN)-based methods. However, we argue that this conclusion is incomplete and overlooks the challenge of densely…
NoSQL systems are a new generation of databases that aim to handle a large volume of data. However there is a large set of NoSQL systems, each has its own characteristics. Consequently choosing the suitable NoSQL system to handle Tweets is…
Cloud data centers are evolving fast. At the same time, today's large-scale data analytics applications require non-trivial performance tuning that is often specific to the applications, workloads, and data center infrastructure. We propose…
Data lakes spend a significant fraction of query execution time on scanning data from remote, disaggregated storage. Decoding alone accounts for 46% of runtime when running TPC-H directly on Parquet files. To address this bottleneck, we…
The plethora of graphs and relational data give rise to many interesting graph-relational queries in various domains, e.g., finding related proteins satisfying relational predicates in a biological network. The maturity of RDBMSs motivated…
In the wake of rapid advancements in artificial intelligence (AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB (AIxDB) promises a new generation of data systems, which will relieve the…
Relational databases, organized into tables connected by primary-foreign key relationships, are a common format for organizing data. Making predictions on relational data often involves transforming them into a flat tabular format through…