Inverse Query Research Articles

IDLIQ: An Incremental Deterministic Finite Automaton Learning Algorithm Through Inverse Queries for Regular Grammar Inference.

We present an efficient incremental learning algorithm for Deterministic Finite Automaton (DFA) with the help of inverse query (IQ) and membership query (MQ). This algorithm is an extension of the Identification of Regular Languages (ID) algorithm from a complete to an incremental learning setup. The learning algorithm learns by making use of a set of labeled examples and by posing queries to a knowledgeable teacher, which is equipped to answer IQs along with MQs and equivalence query. Based on the examples (elements of the live complete set) and responses against IQs from the minimally adequate teacher (MAT), the learning algorithm constructs the hypothesis automaton, consistent with all observed examples. The Incremental DFA Learning algorithm through Inverse Queries (IDLIQ) takes time complexity in the presence of a MAT and ensures convergence to a minimal representation of the target DFA with finite number of labeled examples. Existing incremental learning algorithms; the Incremental ID, the Incremental Distinguishing Strings have polynomial (cubic) time complexity in the presence of a MAT. Therefore, sometimes, these algorithms even fail to learn large complex software systems. In this research work, we have reduced the complexity (from cubic to square form) of the DFA learning in an incremental setup. Finally, we prove the correctness and termination of the IDLIQ algorithm.

A Reinforcement Learning Based Grammatical Inference Algorithm Using Block-Based Delta Inverse Strategy

A resurgent interest for grammatical inference aka automaton learning has emerged in several intriguing areas of computer sciences such as machine learning, software engineering, robotics and internet of things. An automaton learning algorithm commonly uses queries to learn the regular grammar of a Deterministic Finite Automaton (DFA). These queries are posed to a Minimum Adequate Teacher (MAT) by the learner (Learning Algorithm). The membership and equivalence queries which the learning algorithm may pose, are often capable of having their answers provided by the MAT. The three main categories of learning algorithms are incremental, sequential, and complete learning algorithms. In the presence of a MAT, the time complexity of existing DFA learning algorithms is polynomial. Therefore, in some applications these algorithms may fail to learn the system. In this study, we have reduced the time complexity of DFA learning from polynomial to logarithmic form. For this, we propose an efficient complete DFA learning algorithm; the Block based DFA Learning through Inverse Query (BDLIQ) using block based delta inverse strategy, which is based on the idea of inverse queries that John Hopcroft introduced for state minimization of a DFA. The BDLIQ algorithm possess <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(\vert \Sigma \vert N.log N)$ </tex-math></inline-formula> complexity when a MAT is available. The MAT is also made capable of responding to inverse queries. We provide theoretical and empirical analysis of the proposed algorithm. Results show that our suggested approach for complete learning; BDLIQ algorithm, is more efficient than the ID algorithm in terms of time complexity.

Query Representation with Global Consistency on User Click Graphs

The Internet of Things aggregates large-scale query logs. A query log is generally represented as a bipartite graph on a query set and a URL set. Most of the traditional methods used the raw click frequency to weigh the link between a query and a URL on the click graph. In order to address the disadvantages of raw click frequency, researchers proposed the entropy-biased model, which incorporates raw click frequency with inverse query frequency of the URL as the weighting scheme for query representation. In this paper, we observe that the inverse query frequency can be considered a global property of the URL on the click graph, which is more informative than raw click frequency, which can be considered a local property of the URL. Based on this insight, we develop the global consistency model for query representation, which utilizes the click frequency and the inverse query frequency of a URL in a consistent manner. Furthermore, we propose a new scheme called inverse URL frequency as an effective way to capture the global property of a URL. Experiments have been conducted on the AOL search engine log data. The result shows that our global consistency model achieved better performance than the current models.

Spatial inverse query processing

Traditional spatial queries return, for a given query object q, all database objects that satisfy a given predicate, such as epsilon range and k-nearest neighbors. This paper defines and studies inverse spatial queries, which, given a subset of database objects Q and a query predicate, return all objects which, if used as query objects with the predicate, contain Q in their result. We first show a straightforward solution for answering inverse spatial queries for any query predicate. Then, we propose a filter-and-refinement framework that can be used to improve efficiency. We show how to apply this framework on a variety of inverse queries, using appropriate space pruning strategies. In particular, we propose solutions for inverse epsilon range queries, inverse k-nearest neighbor queries, and inverse skyline queries. Furthermore, we show how to relax the definition of inverse queries in order to ensure non-empty result sets. Our experiments show that our framework is significantly more efficient than naive approaches.

Flexible Provenance Tracing

The description of the origins of a piece of data and the transformations by which it arrived in a database is termed the data provenance. The importance of data provenance has already been widely recognized in database community. The two major approaches to representing provenance information use annotations and inversion. While annotation is metadata pre-computed to include the derivation history of a data product, the inversion method finds the source data based on the situation that some derivation process can be inverted. Annotations are flexible to represent diverse provenance metadata but the complete provenance data may outsize data itself. Inversion method is concise by using a single inverse query or function but the provenance needs to be computed on-the-fly. This paper proposes a new provenance representation which is a hybrid of annotation and inversion methods in order to achieve combined advantage. This representation is adaptive to the storage constraint and the response time requirement of provenance inversion on-the-fly.

Combined shape-material sensitivity approach for elastic-wave identification of penetrable obstacles

This study deals with elastic-wave identification of heterogeneities (inclusions) in an otherwise homogeneous "reference" solid from limited-aperture measurements taken on its surface. On adopting the boundary integral equation (BIE) framework for elastodynamic scattering, the inverse query is cast as a minimization problem involving experimental observations and their simulations for a trial inclusion defined through its boundary, elastic moduli, and mass density. Expressions for the shape and material sensitivities of the misfit functional are obtained via the adjoint field approach and direct differentiation of the governing BIE's, respectively. A constrained nonlinear optimization framework based on the direct BIE method and an augmented Lagrangian is implemented. Numerical results for the reconstruction of an ellipsoidal defect in a semi-infinite solid show the effectiveness of the proposed shape-material sensitivity formulation, which constitutes an essential computational component.

Elastic-wave identification of penetrable obstacles using shape-material sensitivity framework

This study deals with elastic-wave identification of discrete heterogeneities (inclusions) in an otherwise homogeneous “reference” solid from limited-aperture waveform measurements taken on its surface. On adopting the boundary integral equation (BIE) framework for elastodynamic scattering, the inverse query is cast as a minimization problem involving experimental observations and their simulations for a trial inclusion that is defined through its boundary, elastic moduli, and mass density. For an optimal performance of the gradient-based search methods suited to solve the problem, explicit expressions for the shape (i.e. boundary) and material sensitivities of the misfit functional are obtained via the adjoint field approach and direct differentiation of the governing BIEs. Making use of the message-passing interface, the proposed sensitivity formulas are implemented in a data-parallel code and integrated into a nonlinear optimization framework based on the direct BIE method and an augmented Lagrangian whose inequality constraints are employed to avoid solving forward scattering problems for physically inadmissible (or overly distorted) trial inclusion configurations. Numerical results for the reconstruction of an ellipsoidal defect in a semi-infinite solid show the effectiveness of the proposed shape-material sensitivity formulation, which constitutes an essential computational component of the defect identification algorithm.