374 research outputs found
F-IVM: Learning over Fast-Evolving Relational Data
F-IVM is a system for real-time analytics such as machine learning
applications over training datasets defined by queries over fast-evolving
relational databases. We will demonstrate F-IVM for three such applications:
model selection, Chow-Liu trees, and ridge linear regression.Comment: SIGMOD DEMO 2020, 5 page
Trade-offs in Static and Dynamic Evaluation of Hierarchical Queries
We investigate trade-offs in static and dynamic evaluation of hierarchical
queries with arbitrary free variables. In the static setting, the trade-off is
between the time to partially compute the query result and the delay needed to
enumerate its tuples. In the dynamic setting, we additionally consider the time
needed to update the query result in the presence of single-tuple inserts and
deletes to the input database.
Our approach observes the degree of values in the database and uses different
computation and maintenance strategies for high-degree and low-degree values.
For the latter it partially computes the result, while for the former it
computes enough information to allow for on-the-fly enumeration.
The main result of this work defines the preprocessing time, the update time,
and the enumeration delay as functions of the light/heavy threshold and of the
factorization width of the hierarchical query. By conveniently choosing this
threshold, our approach can recover a number of prior results when restricted
to hierarchical queries.
For a restricted class of hierarchical queries, our approach can achieve
worst-case optimal update time and enumeration delay conditioned on the Online
Matrix-Vector Multiplication Conjecture.Comment: Technical Report; 52 pages. The updated version contains: new
diagrams and plots summarizing known results and putting the results of the
paper into context; introduction of delta_i-hieararchical queries, for any
non-negative integer i; optimality results for delta_0- and
delta_1-hieararchical querie
Model-Driven Engineering in the Large: Refactoring Techniques for Models and Model Transformation Systems
Model-Driven Engineering (MDE) is a software engineering paradigm that
aims to increase the productivity of developers by raising the
abstraction level of software development. It envisions the use of
models as key artifacts during design, implementation and deployment.
From the recent arrival of MDE in large-scale industrial software
development â a trend we refer to as MDE in the large â, a set of
challenges emerges: First, models are now developed at distributed
locations, by teams of teams. In such highly collaborative settings, the
presence of large monolithic models gives rise to certain issues, such
as their proneness to editing conflicts. Second, in large-scale system
development, models are created using various domain-specific modeling
languages. Combining these models in a disciplined manner calls for
adequate modularization mechanisms. Third, the development of models is
handled systematically by expressing the involved operations using model
transformation rules. Such rules are often created by cloning, a
practice related to performance and maintainability issues.
In this thesis, we contribute three refactoring techniques, each aiming
to tackle one of these challenges. First, we propose a technique to
split a large monolithic model into a set of sub-models. The aim of this
technique is to enable a separation of concerns within models, promoting
a concern-based collaboration style: Collaborators operate on the
submodels relevant for their task at hand. Second, we suggest a
technique to encapsulate model components by introducing modular
interfaces in a set of related models. The goal of this technique is to
establish modularity in these models. Third, we introduce a refactoring
to merge a set of model transformation rules exhibiting a high degree of
similarity. The aim of this technique is to improve maintainability and
performance by eliminating the drawbacks associated with cloning. The
refactoring creates variability-based rules, a novel type of rule
allowing to capture variability by using annotations.
The refactoring techniques contributed in this work help to reduce the
manual effort during the refactoring of models and transformation rules
to a large extent. As indicated in a series of realistic case studies,
the output produced by the techniques is comparable or, in the case of
transformation rules, partly even preferable to the result of manual
refactoring, yielding a promising outlook on the applicability in
real-world settings
Architecture independent environment for developing engineering software on MIMD computers
Engineers are constantly faced with solving problems of increasing complexity and detail. Multiple Instruction stream Multiple Data stream (MIMD) computers have been developed to overcome the performance limitations of serial computers. The hardware architectures of MIMD computers vary considerably and are much more sophisticated than serial computers. Developing large scale software for a variety of MIMD computers is difficult and expensive. There is a need to provide tools that facilitate programming these machines. First, the issues that must be considered to develop those tools are examined. The two main areas of concern were architecture independence and data management. Architecture independent software facilitates software portability and improves the longevity and utility of the software product. It provides some form of insurance for the investment of time and effort that goes into developing the software. The management of data is a crucial aspect of solving large engineering problems. It must be considered in light of the new hardware organizations that are available. Second, the functional design and implementation of a software environment that facilitates developing architecture independent software for large engineering applications are described. The topics of discussion include: a description of the model that supports the development of architecture independent software; identifying and exploiting concurrency within the application program; data coherence; engineering data base and memory management
Maintaining Triangle Queries under Updates
We consider the problem of incrementally maintaining the triangle queries
with arbitrary free variables under single-tuple updates to the input
relations. We introduce an approach called IVM that exhibits a
trade-off between the update time, the space, and the delay for the enumeration
of the query result, such that the update time ranges from the square root to
linear in the database size while the delay ranges from constant to linear
time. IVM achieves Pareto worst-case optimality in the update-delay
space conditioned on the Online Matrix-Vector Multiplication conjecture. It is
strongly Pareto optimal for the triangle queries with zero or three free
variables and weakly Pareto optimal for the triangle queries with one or two
free variables.Comment: 47 pages, 18 figure
Book of Abstracts of the Sixth SIAM Workshop on Combinatorial Scientific Computing
Book of Abstracts of CSC14 edited by Bora UçarInternational audienceThe Sixth SIAM Workshop on Combinatorial Scientific Computing, CSC14, was organized at the Ecole Normale Supérieure de Lyon, France on 21st to 23rd July, 2014. This two and a half day event marked the sixth in a series that started ten years ago in San Francisco, USA. The CSC14 Workshop's focus was on combinatorial mathematics and algorithms in high performance computing, broadly interpreted. The workshop featured three invited talks, 27 contributed talks and eight poster presentations. All three invited talks were focused on two interesting fields of research specifically: randomized algorithms for numerical linear algebra and network analysis. The contributed talks and the posters targeted modeling, analysis, bisection, clustering, and partitioning of graphs, applied in the context of networks, sparse matrix factorizations, iterative solvers, fast multi-pole methods, automatic differentiation, high-performance computing, and linear programming. The workshop was held at the premises of the LIP laboratory of ENS Lyon and was generously supported by the LABEX MILYON (ANR-10-LABX-0070, Université de Lyon, within the program ''Investissements d'Avenir'' ANR-11-IDEX-0007 operated by the French National Research Agency), and by SIAM
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