70 citations · 83 across the 8 of their papers we have counts for
12 papers
STEER: Reducing Inference Cost in Relational Foundation Models through Semantically Informed Sampling
Abdalla Mohamed, Ashraf Aboulnaga
Relational foundation models (RFMs) are pretrained once on a collection of relational databases and prediction tasks, and then applied zero-shot to previously unseen databases and…
MetaSieve: Faster Relational Deep Learning through SQL-Based Metapath Selection
Fahim Shahriar Khan, Ashraf Aboulnaga
Relational Deep Learning (RDL) is an effective approach to machine learning over multi-table relational databases. In RDL, a database is modeled as a graph in which each row is a n…
An LLM-Guided Query-Aware Inference System for GNN Models on Large Knowledge Graphs
Waleed Afandi, Hussein Abdallah, Ashraf Aboulnaga +1
Efficient inference for graph neural networks (GNNs) on large knowledge graphs (KGs) is essential for many real-world applications. GNN inference queries are computationally expens…
gHAWK: Local and Global Structure Encoding for Scalable Training of Graph Neural Networks on Knowledge Graphs
Humera Sabir, Fatima Farooq, Ashraf Aboulnaga
Knowledge Graphs (KGs) are a rich source of structured, heterogeneous data, powering a wide range of applications. A common approach to leverage this data is to train a graph neura…
Enhancing Computation Pushdown for Cloud OLAP Databases
Yifei Yang, Xiangyao Yu, Marco Serafini +2
Network is a major bottleneck in modern cloud databases that adopt a storage-disaggregation architecture. Computation pushdown is a promising solution to tackle this issue, which o…
PushdownDB: Accelerating a DBMS using S3 Computation
Xiangyao Yu, Matt Youill, Matthew Woicik +4
This paper studies the effectiveness of pushing parts of DBMS analytics queries into the Simple Storage Service (S3) engine of Amazon Web Services (AWS), using a recently released…
RDFFrames: Knowledge Graph Access for Machine Learning Tools
Aisha Mohamed, Ghadeer Abuoda, Abdurrahman Ghanem +2
Knowledge graphs represented as RDF datasets are integral to many machine learning applications. RDF is supported by a rich ecosystem of data management systems and tools, most not…
LiveGraph: A Transactional Graph Storage System with Purely Sequential Adjacency List Scans
Xiaowei Zhu, Guanyu Feng, Marco Serafini +5
The specific characteristics of graph workloads make it hard to design a one-size-fits-all graph storage system. Systems that support transactional updates use data structures with…
Link Prediction via Higher-Order Motif Features
Ghadeer Abuoda, Gianmarco De Francisci Morales, Ashraf Aboulnaga
Link prediction requires predicting which new links are likely to appear in a graph. Being able to predict unseen links with good accuracy has important applications in several dom…
Sapphire: Querying RDF Data Made Simple
Ahmed El-Roby, Khaled Ammar, Ashraf Aboulnaga +1
RDF data in the linked open data (LOD) cloud is very valuable for many different applications. In order to unlock the full value of this data, users should be able to issue complex…
Arabesque: A System for Distributed Graph Mining - Extended version
Carlos H. C. Teixeira, Alexandre J. Fonseca, Marco Serafini +3
Distributed data processing platforms such as MapReduce and Pregel have substantially simplified the design and deployment of certain classes of distributed graph analytics algorit…
ReStore: Reusing Results of MapReduce Jobs
Iman Elghandour, Ashraf Aboulnaga
Analyzing large scale data has emerged as an important activity for many organizations in the past few years. This large scale data analysis is facilitated by the MapReduce program…