Sliced Table Constraints: Combining Compression and Tabular Reduction

Gharbi, Nebras; Hémery, Fred; Lecoutre, Christophe; Roussel, Olivier

doi:10.1007/978-3-319-07046-9_9

Cited by 16 publications

(7 citation statements)

References 15 publications

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“…-BDDs in various flavors ((Knuth 2011)) -Compression into c-tuples and constraint slicing ((Gharbi et al 2014;Katsirelos and Walsh 2007)) -Read optimized databases (such as column-oriented databases ((Stonebraker et al 2005))).…”

Section: Discussionmentioning

confidence: 99%

Managing variants of a personalized product

Haag¹

2016

J Intell Inf Syst

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A product variant table is a table that lists legal combinations of product features. Variant tables can be used to constrain the variability offered for a personalized product. The concept of such a table is easy to understand. Hence, variant tables are natural to use when ensuring the completeness and correctness of a quote/order for a customizable product. They are also used to filter out inadmissible choices for features in an interactive specification (configuration) process. Variant tables can be maintained as relational (database) tables, using spreadsheets, or in proprietary ways offered by the product modeling environment. Variant tables can become quite large. A way of compressing them is then sought that supports a space-efficient representation and a time-efficient evaluation. The motivation of this work is to develop a simple approach to compress/compile a variant table into an easy to read, but possibly hard to write form that can be deployed in a business setting at acceptable cost and risk in a similar manner as a database. The main result is a simple compression and evaluation scheme for an individual variant table called a Variant Decomposition Diagram (VDD). A VDD supports efficient consistency checks, the filtering of inadmissible features, and iteration over the table. A simple static heuristic for decomposition order is proposed that suggests itself from a "column oriented viewpoint". This heuristic is not always optimal, but it has the advantage of allowing fast compilation of a variant table into a VDD. Compression results for a publicly available model of a Renault Megane are given. With the proposed heuristic the VDD is a specialization of a Zero-suppressed (binary) Decision I want to thank the reviewers and my daughter Laura for substantial constructive criticism of the original manuscript, all of which I have tried to incorporate here. Any remaining shortcomings are my own. Disclaimer: While the motivation for this work lies in my past at SAP and experiences with its product configurators (Blumöhr et al. 2012; Haag 2014), all work on this article and its precursors (Haag 2015a, b) was performed privately after transition into partial retirement without a work obligation at SAP. The implementation is not related to SAP software in any way. It is neither endorsed by SAP nor does it reflect ongoing SAP development.

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Section: Discussionmentioning

confidence: 99%

Managing variants of a personalized product

Haag¹

2016

J Intell Inf Syst

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“…For example, a regular constraint is simply a DFA defining the set of tuples which can be generated by the automata over the variables in the constraint. Similarly, (Gharbi et al 2014), and smarttables (Mairy, Deville, and Lecoutre 2015). For example, Figure 2…”

Section: Preliminariesmentioning

confidence: 99%

“…The shortSTR algorithm (Jefferson and Nightingale 2013) works on short support which compresses table constraint by hiding the variables whose domain values are always supported. STRslice (Gharbi et al 2014) compresses tables by first grouping tuples of a table (slicing) and then decomposing each group (a subtable) into two tables with a smaller arity where the original subtable is obtained from the join of the two smaller tables. Such algorithms are competitive when there exists enough compression, e.g.…”

Section: Simple Tabular Reductionmentioning

confidence: 99%

Generalized Arc Consistency Algorithms for Table Constraints: A Summary of Algorithmic Ideas

Yap

Xia

Wang

2020

AAAI

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Constraint Programming is a powerful paradigm to model and solve combinatorial problems. While there are many kinds of constraints, the table constraint (also called a CSP) is perhaps the most significant—being the most well-studied and has the ability to encode any other constraints defined on finite variables. Thus, designing efficient filtering algorithms on table constraints has attracted significant research efforts. In turn, there have been great improvements in efficiency over time with the evolution and development of AC and GAC algorithms. In this paper, we survey the existing filtering algorithms for table constraint focusing on historically important ideas and recent successful techniques shown to be effective.

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“…Some are based on using particular data structures, like Multi-valued Decision Diagrams (MDDs) [4,24], tries [7] and Deterministic Finite Automata (DFA) [25]. Other approaches attempt to keep a table-like structure, which is made compact by reasoning on Cartesian products and some intentional forms of column restrictions, like short tuples [10], compressed tuples [11,32], sliced tables [8] and smart tables [21].…”

Section: Introductionmentioning

confidence: 99%

Extending Compact-Table to Basic Smart Tables

Verhaeghe¹,

Lecoutre

Deville³

et al. 2017

Lecture Notes in Computer Science

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Table constraints are instrumental in modelling combinatorial problems with Constraint Programming. Recently, Compact-Table (CT) has been proposed and shown to be as an efficient filtering algorithm for table constraints, notably because of bitwise operations. CT has already been extended to handle non-ordinary tables, namely, short tables and/or negative tables. In this paper, we introduce another extension so as to deal with basic smart tables, which are tables containing universal values ( * ), restrictions on values ( = v) bounds (≤ v or ≥ v) and sets (∈ S). Such tables offer the user a better expressiveness and permit to deal efficiently with compressed tuples. Our experiments show a substantial speedup when compression is possible (and a very limited overhead otherwise).

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Sliced Table Constraints: Combining Compression and Tabular Reduction

Cited by 16 publications

References 15 publications

Managing variants of a personalized product

Managing variants of a personalized product

Generalized Arc Consistency Algorithms for Table Constraints: A Summary of Algorithmic Ideas

Extending Compact-Table to Basic Smart Tables

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