Publications by Anastasia Ailamaki

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Emerging scale-out workloads need extensive amounts of computational resources. However, datacenters using modern server hardware face physical constraints in space and power, limiting further expansion and requiring improvements in the computational densi ...

Hybrid Transactional and Analytical Processing (HTAP) systems have become popular in the past decade. HTAP systems allow running transactional and analytical processing workloads on the same data and hardware. As a result, they suffer from workload interfe ...

The recent explosion in the number and size of spatio-temporal data sets from urban environments and social sensors creates new opportunities for data-driven approaches to understand and improve cities. Visual analytics systems like Urbane aim to empower d ...

We consider MapReduce workloads that are produced by analytics applications. In contrast to ad hoc query workloads, analytics applications are comprised of fixed data flows that are run over newly arriving data sets or on different portions of an existing ...

Emerging scale-out workloads require extensive amounts of computational resources. However, data centers using modern server hardware face physical constraints in space and power, limiting further expansion and calling for improvements in the computational ...

Many scientific and geographical applications rely on the efficient execution of spatial joins. Past research has produced several efficient spatial join approaches and while each of them can join two datasets, the problem of efficiently joining two datase ...

The explosion of available data in the last few years has increased the importance of physical database design, since the selection of proper physical structures (e.g. indices, partitions and materialized views) may improve query execution performance by s ...

Database engines face growing scalability challenges as core counts exponentially increase each processor generation, and the efficiency of synchronization primitives used to protect internal data structures is a crucial factor in overall database performa ...

The increasing volume of time-based generated data and the shift in storage technologies suggest that we might need to reconsider indexing. Several workloads - like social and service monitoring - often include attributes with implicit clustering because o ...

Modern data analytical workloads often need to run queries over a large number of tables. An optimal query plan for such queries is crucial for being able to run these queries within acceptable time bounds. However, with queries involving many tables, find ...

We introduce AdaptPD, an automated physical design tool that improves database performance by continuously monitoring changes in the workload and adapting the physical design to suit the incoming workload. Current physical design tools are offline and requ ...

Analytics on modern data analytic and data warehouse systems often need to run large complex queries on increasingly complex database schemas. A lot of progress has been made on executing such complex queries using techniques like scale out query processin ...

Today's scientists are quickly moving from in vitro to in silico experimentation: they no longer analyze natural phenomena in a petri dish, but instead they build models and simulate them. Managing and analyzing the massive amounts of data involved in simu ...

As data collections become larger and larger, users are faced with increasing bottlenecks in their data analysis. More data means more time to prepare the data, to load the data into the database and to execute the desired queries. Many applications alread ...

The constant flux of data and queries alike has been pushing the boundaries of data analysis systems. The increasing size of raw data files has made data loading an expensive operation that delays the data-to-insight time. To alleviate the loading cost, in ...

Modern data management systems aim to provide both cutting-edge functionality and hardware efficiency. With the advent of AI-driven data processing and the post-Moore Law era, traditional memory-bound scale-up data management operations face scalability ch ...

The performance of spatial joins is becoming increasingly important in many applications, particularly in the scientific domain. Several approaches have been proposed for joining spatial datasets on disk and few in main memory. Recent results show that in ...

As the size of data and its heterogeneity increase, traditional database system architecture becomes an obstacle to data analysis. Integrating and ingesting (loading) data into databases is quickly becoming a bottleneck in face of massive data as well as i ...

Nowadays, high-performance transaction processing applications increasingly run on multisocket multicore servers. Such architectures exhibit non-uniform memory access latency as well as non-uniform thread communication costs. Unfortunately, traditional sha ...

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 systems mostly under-utilize the available micro-archi ...

Anastasia Ailamaki

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A Case for Specialized Processors for Scale-Out Workloads

Performance Characterization of HTAP Workloads

Interactive Visual Exploration of Spatio-Temporal Urban Data Sets using Urbane

Same Queries, Different Data: Can we Predict Query Performance?

Quantifying the Mismatch between Emerging Scale-Out Applications and Modern Processors

GIPSY: Joining Spatial Datasets with Contrasting Density

Automated Physical Designers: What You See is (Not) What You Get

A New Look at the Roles of Spinning and Blocking

BF-Tree: Approximate Tree Indexing

Efficient Massively Parallel Join Optimization for Large Queries

Adaptive Physical Design for Curated Archives

Efficient GPU-accelerated Join Optimization for Complex Queries

SCOUT: Prefetching for Latent Structure Following Queries

NoDB in Action: Adaptive Query Processing on Raw Data

Adaptive partitioning and indexing for in situ query processing

Post-Moore's Law Fusion: High-Bandwidth Memory, Accelerators, and Native Half-Precision Processing for CPU-Local Analytics

Configuring Spatial Grids for Efficient Main Memory Joins

Just-In-Time Data Virtualization: Lightweight Data Management with ViDa

ATraPos: Adaptive Transaction Processing on Hardware Islands

Micro-architectural analysis of in-memory OLTP: Revisited