Publications by Anastasia Ailamaki

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The growing demand for data-intensive decision support and the migration to multi-tenant infrastructures put databases under the stress of high analytical query load. The requirement for high throughput contradicts the traditional design of query-at-a-time ...

Industry and academia are continuously becoming more data-driven and data-intensive, relying on the analysis of a wide variety of heterogeneous datasets to gain insights. The different data models and formats pose a significant challenge on performing anal ...

Intuitively, aggressive work sharing among concurrent queries in a database system should always improve performance by eliminating redundant computation or data accesses. We show that, contrary to common intuition, this is not always the case in practice, ...

Online transaction processing (OLTP) workload performance suffers from instruction stalls; the instruction footprint of a typical transaction exceeds by far the capacity of an L1 cache, leading to ongoing cache thrashing. Several proposed techniques remove ...

Traditionally, analytical database engines have used task parallelism provided by modern multisocket multicore CPUs for scaling query execution. Over the past few years, GPUs have started gaining traction as accelerators for processing analytical queries d ...

Non-Volatile Memory (NVM) technologies exhibit 4× the read access latency of conventional DRAM. When the working set does not fit in the processor cache, this latency gap between DRAM and NVM leads to more than 2× runtime increase for queries dominated by ...

Most modern in-memory online transaction processing (OLTP) engines rely on multi-version concurrency control (MVCC) to provide data consistency guarantees in the presence of conflicting data accesses. MVCC improves concurrency by generating a new version o ...

A method for optimizing memory access for database operations is provided. The method may include performing a first database operation by at least executing a first instruction stream associated with the first database operation. The first database opera ...

The shift to multi-core hardware brings new challenges to database systems, as the software parallelism determines performance. Even though database systems traditionally accommodate simultaneous requests, a multitude of synchronization barriers serialize ...

Neuroscientists increasingly use computational tools to build and simulate models of the brain. The amounts of data involved in these simulations are immense and the importance of their efficient management is primordial. One particular problem in analyzin ...

Efficiently querying multiple spatial data sets is a growing challenge for scientists. Astronomers query data sets that contain different types of stars (e.g., dwarfs, giants, stragglers) while neuroscientists query different data sets that model different ...

Understanding micro-architectural behavior is important for efficiently using hardware resources. Recent work has shown that in-memory online transaction processing (OLTP) systems severely underutilize their core micro-architecture resources [29]. Whereas, ...

Index tuning, i.e., selecting the indexes appropriate for a workload, is a crucial problem in database system tuning. In this paper, we solve index tuning for large problem instances that are common in practice, e.g., thousands of queries in the workload, ...

Scientists in all disciplines increasingly rely on simulations to develop a better understanding of the subject they are studying. For example the neuroscientists we collaborate with in the Blue Brain project have started to simulate the brain on a superco ...

The identification of biological signatures of diseases will enable the development of new biologically grounded classifications of brain diseases, leading to a new systematic understanding of their causes, and new diagnostic tools. In this paper we presen ...

Data cleaning is a time-consuming process that depends on the data analysis that users perform. Existing solutions treat data cleaning as a separate offline process that takes place before analysis begins. Applying data cleaning before analysis assumes a p ...

As data analytics is used by an increasing number of applications, data analytics engines are required to execute workloads with increased concurrency, i.e., an increasing number of clients submitting queries. Data management systems designed for data anal ...

Current Approximate Query Processing (AQP) engines are far from silver-bullet solutions, as they adopt several static design decisions that target specific workloads and deployment scenarios. Offline AQP engines target deployments with large storage budget ...

Conventional OLTP systems assign each transaction to a worker thread and that thread accesses data, depending on what the transaction dictates. This thread-to-transaction work assignment policy leads to unpredictable accesses. The unpredictability forces e ...

Data warehouses (DW) have been traditionally optimized for readonly query performance, allowing only offline updates at night, essentially trading off data freshness for performance. The need for 24x7 operations in global markets and the rise of online and ...

Anastasia Ailamaki

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Scalable Multi-Query Execution using Reinforcement Learning

Fast Queries Over Heterogeneous Data Through Engine Customization

To Share or Not To Share?

STREX: Boosting Instruction Cache Reuse in OLTP Workloads Through Stratified Transaction Execution

Hardware-conscious Hash-Joins on GPUs

Bridging the Latency Gap between NVM and DRAM for Latency-bound Operations

One-shot Garbage Collection for In-memory OLTP through Temporality-aware Version Storage

Determining an optimum quantity of interleaved instruction streams of defined coroutines

Aether: A scalable approach to logging

Accelerating Range Queries For Brain Simulations

Efficient Bundled Spatial Range Queries

Micro-architectural Analysis of OLAP: Limitations and Opportunities

CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads

Data-driven Neuroscience: Enabling Breakthroughs Via Innovative Data Management

Towards the Identification of Disease Signatures

Cleaning Denial Constraint Violations through Relaxation

Scaling up analytical queries with column-stores

Taster: Self-Tuning, Elastic and Online Approximate Query Processing

A Data-oriented Transaction Execution Engine and Supporting Tools

MaSM: Efficient Online Updates in Data Warehouses