53 citations · 199 across the 13 of their papers we have counts for
13 papers
OneProvenance: Efficient Extraction of Dynamic Coarse-Grained Provenance from Database Logs [Technical Report]
Fotis Psallidas, Ashvin Agrawal, Chandru Sugunan +6
Provenance encodes information that connects datasets, their generation workflows, and associated metadata (e.g., who or when executed a query). As such, it is instrumental for a w…
End-to-end Optimization of Machine Learning Prediction Queries
Kwanghyun Park, Karla Saur, Dalitso Banda +3
Prediction queries are widely used across industries to perform advanced analytics and draw insights from data. They include a data processing part (e.g., for joining, filtering, c…
Query Processing on Tensor Computation Runtimes
Dong He, Supun Nakandala, Dalitso Banda +7
The huge demand for computation in artificial intelligence (AI) is driving unparalleled investments in hardware and software systems for AI. This leads to an explosion in the numbe…
KEA: Tuning an Exabyte-Scale Data Infrastructure
Yiwen Zhu, Subru Krishnan, Konstantinos Karanasos +12
Microsoft's internal big-data infrastructure is one of the largest in the world -- with over 300k machines running billions of tasks from over 0.6M daily jobs. Operating this infra…
A Tensor Compiler for Unified Machine Learning Prediction Serving
Supun Nakandala, Karla Saur, Gyeong-In Yu +4
Machine Learning (ML) adoption in the enterprise requires simpler and more efficient software infrastructure---the bespoke solutions typical in large web companies are simply unten…
Data Science through the looking glass and what we found there
Fotis Psallidas, Yiwen Zhu, Bojan Karlas +8
The recent success of machine learning (ML) has led to an explosive growth both in terms of new systems and algorithms built in industry and academia, and new applications built by…
Extending Relational Query Processing with ML Inference
Konstantinos Karanasos, Matteo Interlandi, Doris Xin +10
The broadening adoption of machine learning in the enterprise is increasing the pressure for strict governance and cost-effective performance, in particular for the common and cons…
Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML
Ashvin Agrawal, Rony Chatterjee, Carlo Curino +19
Machine learning (ML) has proven itself in high-value web applications such as search ranking and is emerging as a powerful tool in a much broader range of enterprise scenarios inc…
Griffon: Reasoning about Job Anomalies with Unlabeled Data in Cloud-based Platforms
Liqun Shao, Yiwen Zhu, Abhiram Eswaran +9
Microsoft's internal big data analytics platform is comprised of hundreds of thousands of machines, serving over half a million jobs daily, from thousands of users. The majority of…
Query and Resource Optimizations: A Case for Breaking the Wall in Big Data Systems
Alekh Jindal, Lalitha Viswanathan, Konstantinos Karanasos
Modern big data systems run on cloud environments where resources are shared amongst several users and applications. As a result, declarative user queries in these environments nee…
Kaskade: Graph Views for Efficient Graph Analytics
Joana M. F. da Trindade, Konstantinos Karanasos, Carlo Curino +2
Graphs are an increasingly popular way to model real-world entities and relationships between them, ranging from social networks to data lineage graphs and biological datasets. Que…
The ViP2P Platform: XML Views in P2P
Konstantinos Karanasos, Asterios Katsifodimos, Ioana Manolescu +1
The growing volumes of XML data sources on the Web or produced by enterprises, organizations etc. raise many performance challenges for data management applications. In this work,…
RDFViewS: A Storage Tuning Wizard for RDF Applications
François Goasdoué, Konstantinos Karanasos, Julien Leblay +1
In recent years, the significant growth of RDF data used in numerous applications has made its efficient and scalable manipulation an important issue. In this paper, we present RDF…