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Kjetil Nørvåg - One of the best experts on this subject based on the ideXlab platform.

  • The design, implementation, and performance of the V2 temporal Document Database system
    Information and Software Technology, 2004
    Co-Authors: Kjetil Nørvåg
    Abstract:

    Abstract It is now feasible to store previous versions of Documents, and not only the most recent version which has been the traditional approach. This is of interest in a number of application, both temporal Document Databases as well as web archiving systems and temporal XML warehouses. In this paper, we describe the architecture and the implementation of V2, a temporal Document Database system that supports storage, retrieval, and querying of temporal Documents. We also give some performance results from a mini-benchmark run on the V2 prototype.

  • Supporting temporal text-containment queries in temporal Document Databases
    Data & Knowledge Engineering, 2004
    Co-Authors: Kjetil Nørvåg
    Abstract:

    In temporal Document Databases, and temporal XML Databases, temporal text-containment queries are a potential performance bottleneck. In this paper we describe how to manage Documents and index structures in such Databases in a way that makes, temporal text-containment querying feasible. We describe and discuss different index structures that can improve such queries. Three of the alternatives have been implemented in the V2 temporal Document Database system, and the performance of the index structures is studied using temporal web data. The results show that even a very simple time-indexing approach can reduce query cost by up to three orders of magnitude.

  • Algorithms for Granularity Reduction in Temporal Document Databases
    2003
    Co-Authors: Kjetil Nørvåg
    Abstract:

    With rapidly decreasing storage costs temporal Document Databases is now a viable solution in many contexts. However, storing an ever growing Database can still be too costly, and as a consequence it is desirable to be able to physically delete old versions. Traditionally, this has been performed by an operation called vacuuming, where the oldest versions are physically deleted (or migrated from secondary storage to cheaper tertiary storage). However, in temporal Document Databases it is more appropriate to remove intermediate versions instead of removing the oldest versions. We call this operation granularity reduction. In this paper we describe six approaches to granularity reduction, and discuss advantages and disadvantages of these approaches. Three of the approaches have been implemented into the V2 temporal Document Database system, and in this context we discuss the cost of applying the approaches

Denys Poshyvanyk - One of the best experts on this subject based on the ideXlab platform.

  • how do developers Document Database usages in source code
    Automated Software Engineering, 2015
    Co-Authors: Mario Linaresvasquez, Christopher Vendome, Denys Poshyvanyk
    Abstract:

    Database-centric applications (DCAs) usually contain a large number of tables, attributes, and constraints describing the underlying data model. Understanding how Database tables and attributes are used in the source code along with the constraints related to these usages is an important component of DCA maintenance. However, Documenting Database-related operations and their constraints in the source code is neither easy nor common in practice. In this paper, we present a two-fold empirical study aimed at identifying how developers Document Database usages at source code method level. In particular, (i) we surveyed open source developers to understand their practices on Documenting Database usages in source code, and (ii) we mined a large set of open source projects to measure to what extent Database-related methods are commented and if these comments are updated during evolution. Although 58% of the developers claimed to find value in method comments describing Database usages, our findings suggest that 77% of 33K+ methods in 3.1K+ open-source Java projects with Database accesses were completely unDocumented.

  • ASE - How do developers Document Database usages in source code
    2015 30th IEEE ACM International Conference on Automated Software Engineering (ASE), 2015
    Co-Authors: Mario Linares-vásquez, Christopher Vendome, Denys Poshyvanyk
    Abstract:

    Database-centric applications (DCAs) usually contain a large number of tables, attributes, and constraints describing the underlying data model. Understanding how Database tables and attributes are used in the source code along with the constraints related to these usages is an important component of DCA maintenance. However, Documenting Database-related operations and their constraints in the source code is neither easy nor common in practice. In this paper, we present a two-fold empirical study aimed at identifying how developers Document Database usages at source code method level. In particular, (i) we surveyed open source developers to understand their practices on Documenting Database usages in source code, and (ii) we mined a large set of open source projects to measure to what extent Database-related methods are commented and if these comments are updated during evolution. Although 58% of the developers claimed to find value in method comments describing Database usages, our findings suggest that 77% of 33K+ methods in 3.1K+ open-source Java projects with Database accesses were completely unDocumented.

Shanfeng Zhu - One of the best experts on this subject based on the ideXlab platform.

  • DASFAA Workshops - Towards a Pattern-Based Query Language for Document Databases
    Database Systems for Advanced Applications, 2015
    Co-Authors: Zhengqi Liu, Mengchi Liu, Shanfeng Zhu
    Abstract:

    Document Databases are becoming popular, but how to present complex Document query to obtain useful information from the Document remains an important topic to study. In this paper, we describe the design issues of a pattern-based Document Database query language named JPQ, which uses various expressive patterns to extract and construct Document fragments following a JSON-like Document data model. It adopts tree-like extraction patterns with a coherent pattern composition mechanism to extract data elements from hierarchically structured Documents and maintain the logical relationships among the elements. Based on these relationships, JPQ deploys a deductive mechanism to declaratively specify the data transformation requests and considers also data filtering on hierarchical data structure.

  • Design Issues of JPQ: a Pattern-based Query Language for Document Databases.
    arXiv: Databases, 2015
    Co-Authors: Mengchi Liu, Shanfeng Zhu
    Abstract:

    Document Databases are becoming popular, but how to present complex Document query to obtain useful information from the Document remains an important topic to study. In this paper, we describe the design issues of a pattern-based Document Database query language named JPQ. JPQ uses various expressive patterns to extract and construct Document fragments following a JSON-like Document data model. It adopts tree-like extraction patterns with a coherent pattern composition mechanism to extract data elements from hierarchically structured Documents and maintain the logical relationships among the elements. Based on these relationships, JPQ deploys a deductive mechanism to declaratively specify the data transformation requests and considers also data filtering on hierarchical data structure. We use various examples to show the features of the language and to demonstrate its expressiveness and declarativeness in presenting complex Document queries.

Thierry Paquet - One of the best experts on this subject based on the ideXlab platform.

  • Keyword spotting in handwritten Documents based on a generic text line HMM and a SVM verification
    2015
    Co-Authors: Yousri Kessentini, Thierry Paquet
    Abstract:

    In this paper, we propose a novel system for keyword spotting in handwritten Documents. Our approach proceeds in two steps: first a generic text line HMM provides a simple and flexible tool to localize the keyword and its character boundaries. In the second step, a SVM based verification system estimates and combines the character probabilities to provide keyword confidence scores which are further combined with the HMM score. The system has been evaluated on a public handwritten Document Database used for the 2011 ICDAR handwriting recognition competitions and shows that the verification stage improves the performance and outperforms some other state-of-the-art approaches.

  • A Deep HMM model for multiple keywords spotting in handwritten Documents
    Pattern Analysis and Applications, 2015
    Co-Authors: Simon Thomas, Clement Chatelain, Thierry Paquet, Laurent Heutte, Yousri Kessentini
    Abstract:

    In this paper, we propose a query by string word spotting system able to extract arbitrary key-words in handwritten Documents, taking both segmen-tation and recognition decisions at the line level. The system relies on the combination of a HMM line model made of keyword and non-keyword (filler) models, with a deep neural network (DNN) that estimates the state-dependent observation probabilities. Experiments are carried out on RIMES Database, an unconstrained hand-written Document Database that is used for benchmark-ing different handwriting recognition tasks. The ob-tained results show the superiority of the proposed frame-work over the classical GMM-HMM and standard HMM hybrid architectures.

  • Word Spotting and Regular Expression Detection in Handwritten Documents
    2013
    Co-Authors: Yousri Kessentini, Clement Chatelain, Thierry Paquet
    Abstract:

    In this paper, we propose a novel system for word spotting and regular expression detection in Handwritten Documents. The proposed approach is lexicon-free, i.e., able to spot arbitrary keywords that are not required to be known at the training stage. Furthermore, the proposed system is segmentation-free, i.e., text lines are not required to be segmented into words. The originalities of our approach is twofold. First we propose a new filler model which allows to speed-up the decoding process. Second, we extend the methodology to search for regular expressions. The system has been evaluated on a public handwritten Document Database used for the 2011 ICDAR handwriting recognition competitions.

  • A categorization system for handwritten Documents
    International Journal on Document Analysis and Recognition (IJDAR), 2012
    Co-Authors: Thierry Paquet, Guillaume Koch, Laurent Heutte, Clement Chatelain
    Abstract:

    This paper presents a complete system able to categorize handwritten Documents, i.e. to classify Documents according to their topic. The categorization approach is based on the detection of some discriminative keywords prior to the use of the well-known tf-idf representation for Document categorization. Two keyword extraction strategies are explored. The first one proceeds to the recognition of the whole Document. However, the performance of this strategy strongly decreases when the lexicon size increases. The second strategy only extracts the discriminative keywords in the handwritten Documents. This information extraction strategy relies on the integration of a rejection model (or anti-lexicon model) in the recognition system. Experiments have been carried out on an unconstrained handwritten Document Database coming from an industrial application concerning the processing of incoming mails. Results show that the discriminative keyword extraction system leads to better recall/precision tradeoffs than the full recognition strategy. The keyword extraction strategy also outperforms the full recognition strategy for the categorization task.

  • Detection and Recognition of Erasures in On-Line Captured Paper Forms
    Pattern Recognition Letters, 2007
    Co-Authors: Thierry Paquet, Alain Wiart, Laurent Heutte
    Abstract:

    This paper presents a method to automatically locate and recognize erasures in on-line captured handwritten Documents in order to avoid a subsequent misrecognition of characters and words. We offer a comprehensive definition of the ambiguous concept of erasure in handwriting that results in a more accurate characterization of the different types of erasures. Thanks to this characterization, a preprocessing step, placed upstream of the word recognition engine, enables to classify through an MLP each couple of connected strokes as being an erasure or not using a low-level feature set. We evaluate our system on a real handwritten Document Database and show how our system can be tuned to operate in accordance with various recognition engines thus leading to high performance in erasure detection and recognition.

Laurent Heutte - One of the best experts on this subject based on the ideXlab platform.

  • A Deep HMM model for multiple keywords spotting in handwritten Documents
    Pattern Analysis and Applications, 2015
    Co-Authors: Simon Thomas, Clement Chatelain, Thierry Paquet, Laurent Heutte, Yousri Kessentini
    Abstract:

    In this paper, we propose a query by string word spotting system able to extract arbitrary key-words in handwritten Documents, taking both segmen-tation and recognition decisions at the line level. The system relies on the combination of a HMM line model made of keyword and non-keyword (filler) models, with a deep neural network (DNN) that estimates the state-dependent observation probabilities. Experiments are carried out on RIMES Database, an unconstrained hand-written Document Database that is used for benchmark-ing different handwriting recognition tasks. The ob-tained results show the superiority of the proposed frame-work over the classical GMM-HMM and standard HMM hybrid architectures.

  • Spot it! Finding words and patterns in historical Documents
    2013
    Co-Authors: Vladislavs Dovgalecs, Alexandre Burnett, Pierrick Tranouez, Stéphane Nicolas, Laurent Heutte
    Abstract:

    We propose a system designed to spot either words or patterns, based on a user made query. Employing a two stage approach, it takes advantage of the descriptive power of the Bag of Visual Words (BOVW) representation and the discriminative power of the proposed Longest Weighted Profile (LWP) algorithm. First, we try to identify the zones of images that share common characteristics with the query as summed up in a BOVW. Then, we filter these zones using the LWP introducing spatial constraints extracted from the query. We have validated our system on the George Washington handwritten Document Database for word spotting, and medieval manuscripts from the DocExplore project for pattern spotting.

  • A categorization system for handwritten Documents
    International Journal on Document Analysis and Recognition (IJDAR), 2012
    Co-Authors: Thierry Paquet, Guillaume Koch, Laurent Heutte, Clement Chatelain
    Abstract:

    This paper presents a complete system able to categorize handwritten Documents, i.e. to classify Documents according to their topic. The categorization approach is based on the detection of some discriminative keywords prior to the use of the well-known tf-idf representation for Document categorization. Two keyword extraction strategies are explored. The first one proceeds to the recognition of the whole Document. However, the performance of this strategy strongly decreases when the lexicon size increases. The second strategy only extracts the discriminative keywords in the handwritten Documents. This information extraction strategy relies on the integration of a rejection model (or anti-lexicon model) in the recognition system. Experiments have been carried out on an unconstrained handwritten Document Database coming from an industrial application concerning the processing of incoming mails. Results show that the discriminative keyword extraction system leads to better recall/precision tradeoffs than the full recognition strategy. The keyword extraction strategy also outperforms the full recognition strategy for the categorization task.

  • Detection and Recognition of Erasures in On-Line Captured Paper Forms
    Pattern Recognition Letters, 2007
    Co-Authors: Thierry Paquet, Alain Wiart, Laurent Heutte
    Abstract:

    This paper presents a method to automatically locate and recognize erasures in on-line captured handwritten Documents in order to avoid a subsequent misrecognition of characters and words. We offer a comprehensive definition of the ambiguous concept of erasure in handwriting that results in a more accurate characterization of the different types of erasures. Thanks to this characterization, a preprocessing step, placed upstream of the word recognition engine, enables to classify through an MLP each couple of connected strokes as being an erasure or not using a low-level feature set. We evaluate our system on a real handwritten Document Database and show how our system can be tuned to operate in accordance with various recognition engines thus leading to high performance in erasure detection and recognition.

  • Erasure Extraction in On-Line Captured Paper Forms
    2006
    Co-Authors: Alain Wiart, Thierry Paquet, Laurent Heutte
    Abstract:

    In this paper, we describe a preprocessing system which locates erasures in on-line captured handwritten Documents. Our approach is conceived so as to be placed upstream of the handwritten recognition engine. This system classifies each couple of connected strokes using a low level feature set and a multi layer perceptron classifier. One part of this study gives an efficient definition of erasure, which results in splitting the two original classes of the problem into nineteen more accurate sub-classes. The tunable tolerance level of the system provides a good flexibility to operate in accordance with various recognition engines. We evaluate our system on a real Document Database and present encouraging performance results.