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We work on the foundations of the next generation of data analytics and data-centric programming systems. These systems extend ideas from programming languages, artificial intelligence, data management systems, and theory. Data-intensive applications are increasingly more demanding in sophisticated algorithms to represent, store, query, process, analyse and interpret data. We build and study data-centric programming methods and systems at the core of artificial intelligence applications. Challenges include the robust and efficient processing of large amounts of structured, heterogeneous, and distributed data.
Last activity report : 2022
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Algebraic Foundations for Distributed Query Evaluation
Distributed Evaluation of Graph Queries using Recursive Relational Algebra.
We have investigated the distributed evaluation of
An Algebra with a Fixpoint Operator for Distributed Data Collections.
Big data programming frameworks are becoming increasingly important for the development of applications, for which performance and scalability are critical. In those complex frameworks, optimizing code by hand is hard and time-consuming, making automated optimization particularly necessary. In order to automate optimization, a prerequisite is to find suitable abstractions to represent programs; for instance, algebras based on monads or monoids to represent distributed data collections. Currently, however, such algebras do not represent recursive programs in a way which allows analyzing or rewriting them. We extend a monoid algebra with a fixpoint operator for representing recursion as a first class citizen and show how it allows new optimizations. The fixpoint operator is suitable for modeling recursive computations with distributed data collections. We show that under reasonable conditions this fixpoint can be evaluated by parallel loops with one final merge rather than by a global loop requiring network overhead after each iteration. We also propose several rewrite rules, showing when and how filters can be pushed through recursive terms, and how to filter inside a fixpoint before a join. Experiments with the Spark platform illustrate performance gains brought by these systematic optimizations 5, 3.
Query Plan Enumeration
Efficient Enumeration of Recursive Plans in Transformation-based Query Optimizers.
Query optimizers built on the transformation-based Volcano/Cascades framework are used in many database systems. Transformations proposed earlier on the logical query dag (LQDAG) data structure, which is key in such a framework, focus only on recursion-free queries. We propose the recursive logical query dag (RLQDAG) which extends the LQDAG with the ability to capture and transform recursive queries, leveraging recent developments in recursive relational algebra. Specifically, this extension includes: (i) the ability of capturing and transforming sets of recursive relational terms thanks to (ii) annotated equivalence nodes used for guiding transformations that are more complex in the presence of recursion; and (iii) RLQDAG rewrite rules that transform sets of subterms in a grouped manner, instead of transforming individual terms in a sequential manner; and that (iv) incrementally update the necessary annotations. Core concepts of the RLQDAG are formalized using a syntax and formal semantics with a particular focus on subterm sharing and recursion. The result is a clean generalization of the LQDAG transformation-based approach, enabling more efficient explorations of plan spaces for recursive queries. An implementation of the proposed approach shows significant performance gains compared to the state-of-the-art 6.
Exploring Property Graphs with Recursive Path Patterns.
We demonstrate a system for recursive query answering over property graphs. The novelty of the system resides in its ability to optimize and efficiently answer recursive path patterns in queries for property graphs. The system is based on a complete implementation of the
Data Cleaning and Exchange
Provenance-aware Discovery of Functional Dependencies on Integrated Views.
The automatic discovery of functional dependencies(FDs) has been widely studied as one of the hardest problems in data profiling. Existing approaches have focused on making the FD computation efficient while inspecting single relations at a time. In this paper, for the first time we address the problem of inferring FDs for multiple relations as they occur in integrated views by solely using the functional dependencies of the base relations of the view itself. To this purpose, we leverage logical inference and selective mining and show that we can discover most of the exact FDs from the base relations and avoid the full computation of the FDs for the integrated view itself, while at the same time preserving the lineage of FDs of base relations. We propose algorithms to speedup the inferred FD discovery process and mine FDs on-the-fly only from necessary data partitions. We present InFine(INferred FunctIoNal dEpendency), an end-to-end solution to discover inferred FDs on integrated views by leveraging provenance information of base relations. Our experiments on a range of real-world and synthetic datasets demonstrate the benefits of our method over existing FD discovery methods that need to rerun the discovery process on the view from scratch and cannot exploit lineage information on the FDs. We show that InFine outperforms traditional methods necessitating the full integrated view computation by one to two order of magnitude in terms of runtime. It is also the most memory efficient method while preserving FD provenance information using mainly inference from base table with negligible execution time.
These results were presented at the ICDE 2022 conference 2.
On the Replicability of Knowledge Enhanced Neural Networks in a Graph Neural Network Framework.
In order to extend Knowledge Enhanced Neural Networks, we investigate the replicability of the approach and present a re-implementation of Knowledge Enhanced Neural Networks based on a Graph Neural Network framework (PyTorch Geometric). Knowledge Enhanced Neural Networks integrate prior knowledge in the form of logical formulas into an Artificial Neural Network by adding additional Knowledge Enhancement layers. The obtained results show that the model outperforms pure neural models as well as Neural-Symbolic models. Our long term goal is to be able to address more complex and large-scale knowledge graphs and to benefit from the wide range of functionalities available in PyTorch Geometric. To ensure that our implementation produces the same results, we replicate the original transductive experiments and explain the various challenges and the steps that we went through to reach that goal 8.
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