Publications by Anastasia Ailamaki

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Modern hardware is increasingly complex, requiring increasing effort to understand in order to carefully engineer systems for optimal performance and effective utilization. Moreover, established design principles and assumptions are not portable to modern ...

Instruction-cache misses account for up to 40%; of execution time in online transaction processing (OLTP) database workloads. In contrast to data cache misses, instruction misses cannot be overlapped with out-of-order execution. Chip design limitations do ...

Modern server hardware is increasingly heterogeneous as hardware accelerators, such as GPUs, are used together with multicore CPUs to meet the computational demands of modern data analytics workloads. Unfortunately, query parallelization techniques used by ...

Modern hardware is abundantly parallel and increasingly heterogeneous. The numerous processing cores have non-uniform access latencies to the main memory and processor caches, which causes variability in the communication costs. Unfortunately, database sys ...

Recent studies highlight that traditional transaction processing systems utilize the micro-architectural features of modern processors very poorly. L1 instruction cache and long-latency data misses dominate execution time. As a result, more than half of th ...

As the data volume grows, reducing the query execution times remains an elusive goal. While approximate query processing (AQP) techniques present a principled method to trade off accuracy for faster queries in analytics, the sample creation is often consid ...

Collecting data, extracting value, and combining insights from relational and context-rich sources of many modalities in data processing pipelines presents a challenge for traditional relational DBMS. While relational operators enable declarative and optim ...

Writing correct and efficient recursive SQL queries is exceptionally challenging because recursive queries risk returning incorrect results, throwing runtime exceptions, or never terminating. Failures in recursive query execution fall into two categories: ...

Scaling the performance of shared-everything transaction processing systems to highly-parallel multicore hardware remains a challenge for database system designers. Recent proposals alleviate locking and logging bottlenecks in the system, leaving page latc ...

Today an ever increasing amount of data is collected and analyzed by researchers, businesses, and scientists in data warehouses (DW). In addition to the data size, the number of users and applications querying data grows exponentially. The increasing concu ...

Micro-architectural behavior of traditional disk-based online transaction processing (OLTP) systems has been investigated extensively over the past couple of decades. Results show that traditional OLTP mostly under-utilize the available micro-architectural ...

An increasing number of applications from finance, meteorology, science and others are producing time series as output. The analysis of the vast amount of time series is key to understand the phenomena studied, particularly in the simulation sciences, wher ...

As data continues to be generated at exponentially growing rates in heterogeneous formats, fast analytics to extract meaningful information is becoming increasingly important. Systems widely use in-memory caching as one of their primary techniques to speed ...

Many parallel applications exhibit unpredictable communication between threads, leading to contention for shared objects. The choice of contention management strategy impacts strongly the performance and scalability of these applications: spinning provides ...

Modern analytical engines rely on Approximate Query Processing (AQP) to provide faster response times than the hardware allows for exact query answering. However, existing AQP methods impose steep performance penalties as workload unpredictability increase ...

Data warehouses have been traditionally optimized for read-only 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 oth ...

For several decades, online transaction processing has been one of the main applications that drives innovations in the data management ecosystem, and in turn the database and computer architecture communities. Despite the novel approaches from industry an ...

Machine learning algorithms are widely used today for analytical tasks such as data cleaning, data categorization, or data filtering. At the same time, the rise of social media motivates recent uptake in large scale graph processing. Both categories of alg ...

Today's data deluge enables organizations to collect massive data, and analyze it with an ever-increasing number of concurrent queries. Traditional data warehouses (DW) face a challenging problem in executing this task, due to their query-centric model: ea ...

Scientists in many disciplines have progressively been using simulations to better understand the natural systems they study. Faster hardware, as well as increasingly precise instruments, allow the construction and simulation of progressively advanced mod ...

Anastasia Ailamaki

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Chaosity: Understanding Contemporary NUMA-architectures

Improving instruction cache performance in OLTP

HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines

Characterization of the Impact of Hardware Islands on OLTP

ADDICT: Advanced Instruction Chasing for Transactions

Sampling-Based AQP in Modern Analytical Engines

Optimizing Context-Enhanced Relational Joins

Static Typing Meets Adaptive Optimization: A Unified Approach to Recursive Queries

Scalable and Dynamically Balanced Shared-Everything OLTP with Physiological Partitioning

Reactive and Proactive Sharing Across Concurrent Analytical Queries

Micro-architectural Analysis of In-memory OLTP

RUBIK: Efficient Threshold Queries on Massive Time Series

ReCache: Reactive Caching for Fast Analytics over Heterogeneous Data

Decoupling contention management from scheduling

LAQy: Efficient and Reusable Query Approximations via Lazy Sampling

Online Updates on Data Warehouses via Judicious Use of Solid-State Storage

OLTP in Wonderland -- Where do cache misses come from in major OLTP components?

PREDIcT: Towards Predicting the Runtime of Large Scale Iterative Analytics

Sharing Data and Work Across Concurrent Analytical Queries

Spatial Data Management Challenges in the Simulation Sciences