Existing Query Engines Research Articles

User-defined operators

In recent years, complex data mining and machine learning algorithms have become more common in data analytics. Several specialized systems exist to evaluate these algorithms on ever-growing data sets, which are built to efficiently execute different types of complex analytics queries. However, using these various systems comes at a price. Moving data out of traditional database systems is often slow as it requires exporting and importing data, which is typically performed using the relatively inefficient CSV format. Additionally, database systems usually offer strong ACID guarantees, which are lost when adding new, external systems. This disadvantage can be detrimental to the consistency of the results. Most data scientists still prefer not to use classical database systems for data analytics. The main reason why RDBMS are not used is that SQL is difficult to work with due to its declarative and set-oriented nature, and is not easily extensible. We present User-Defined Operators (UDOs) as a concept to include custom algorithms into modern query engines. Users can write idiomatic code in the programming language of their choice, which is then directly integrated into existing database systems. We show that our implementation can compete with specialized tools and existing query engines while retaining all beneficial properties of the database system.

A fuzzy query engine for suggesting the products based on conformance and asymmetric conjunction

Most of the existing query engines built into modern e-commerce platforms retrieve products by means of text and numerical matching. Conventional query engines for conducting product search suffer from several problems because of which they are not good product recommendation engines. First, they solve tasks without considering semantic uncertainties in users’ questions and in data and therefore do not consider partial matching of query conditions. Users may need to input imprecise information to express their preferences or data describing products can be imprecise as well. Second, existing query engines used for conducting product search tasks do not consider conformance among tuples’ features and aggregation of atomic conditions of different nature. Fuzzy query approaches solve majority of problems related to semantic uncertainty, but do not cover all possible flexibilities to a sufficient level. When user defines sharp conditions (even though he might have values similar to provided ones in mind), entity either fully matches or fundamentally differs, regardless of the similarity between feature's value and the value appearing in conditions. This paper proposes a novel approach to suggesting suitable products based on fuzzy conformance between features appearing in the vector of the ideal product from customer's point of view and the features of existing products. In the second stage, several aggregations of atomic conditions for calculating overall matching between user's needs and available products in a data set are discussed. In addition, our approach is able to manage customers’ requirements expressed by constraints (features which should be met) and wishes (it is nice if these features are met) by the and if possible operator. Thus, asymmetric conjunction is an effective way to capture them. Experiments on a data set of used cars have shown promising results. Proposed fuzzy query engine considers some complex behaviors such as imprecise preferences, vague data in datasets, constraints and wishes in users’ requirements without previous history of queries, suggested answers, feedbacks, gathered ratings and customers’ personal data. It provides application in a specific domain of intelligent systems. The proposed framework can be adapted in a straightforward manner to any system for recommending suitable products, especially for the less-frequently purchased ones. Moreover, businesses that are not able to build or manage complex recommendation systems for gathering customer's behavioral data, as well as customers who are concerned with privacy risks might have benefit from this approach.

Related Axis: The Extension to XPath Towards Effective XML Search

We investigate the limitations of existing XML search methods and propose a new semantics, related relationship, to effectively capture meaningful relationships of data elements from XML data in the absence of structural constraints. Then we make an extension to XPath by introducing a new axis, related axis, to specify the related relationship between query nodes so as to enhance the flexibility of XPath. We propose to reduce the cost of computing the related relationship by a new schema summary that summarizes the related relationship from the original schema without any loss. Based on this schema summary, we introduce two indices to improve the performance of query processing. Our algorithm shows that the evaluation of most queries can be equivalently transformed into just a few selection and value join operations, thus avoids the costly structural join operations. The experimental results show that our method is effective and efficient in terms of comparing the effectiveness of the related relationship with existing keyword search semantics and comparing the efficiency of our evaluation methods with existing query engines.

Using schematically heterogeneous structures

Schematic heterogeneity arises when information that is represented as data under one schema, is represented within the schema (as metadata) in another. Schematic heterogeneity is an important class of heterogeneity that arises frequently in integrating legacy data in federated or data warehousing applications. Traditional query languages and view mechanisms are insufficient for reconciling and translating data between schematically heterogeneous schemas. Higher order query languages, that permit quantification over schema labels, have been proposed to permit querying and restructuring of data between schematically disparate schemas. We extend this work by considering how these languages can be used in practice. Specifically, we consider a restricted class of higher order views and show the power of these views in integrating legacy structures. Our results provide insights into the properties of restructuring transformations required to resolve schematic discrepancies. In addition, we show how the use of these views permits schema browsing and new forms of data independence that are important for global information systems. Furthermore, these views provide a framework for integrating semi-structured and unstructured queries, such as keyword searches, into a structured querying environment. We show how these views can be used with minimal extensions to existing query engines. We give conditions under which a higher order view is usable for answering a query and provide query translation algorithms.