Query Evaluation Research Articles

BOSS - An Architecture for Database Kernel Composition

Composable Database System Research has yielded components such as Apache Arrow for Storage, Meta's Velox for processing and Apache Calcite for query planning. What is lacking, however, is a design for a general, efficient and easy-to-use architecture to connect them. We propose such an architecture. Our proposal is based on the ideas of partial query evaluation and a carefully designed, unified exchange format for query plans and data. We implement the architecture in a system called BOSS 1 that combines the Apache Arrow, the GPU-accelerated compute kernel ArrayFire and the CPU-oriented Velox kernel into a fully-featured relational Data Management System (DMS). We demonstrate that the architecture is general enough to incorporate practically any DMS component, easy-to-use and virtually overhead-free. Based on the architecture, BOSS achieves significant performance improvement over the CPU-only Velox kernel and even outperforms the highly-optimized GPU-only DMS HeavyDB for some queries.

Logical–Mathematical Foundations of a Graph Query Framework for Relational Learning

Relational learning has attracted much attention from the machine learning community in recent years, and many real-world applications have been successfully formulated as relational learning problems. In recent years, several relational learning algorithms have been introduced that follow a pattern-based approach. However, this type of learning model suffers from two fundamental problems: the computational complexity arising from relational queries and the lack of a robust and general framework to serve as the basis for relational learning methods. In this paper, we propose an efficient graph query framework that allows for cyclic queries in polynomial time and is ready to be used in pattern-based learning methods. This solution uses logical predicates instead of graph isomorphisms for query evaluation, reducing complexity and allowing for query refinement through atomic operations. The main differences between our method and other previous pattern-based graph query approaches are the ability to evaluate arbitrary subgraphs instead of nodes or complete graphs, the fact that it is based on mathematical formalization that allows the study of refinements and their complementarity, and the ability to detect cyclic patterns in polynomial time. Application examples show that the proposed framework allows learning relational classifiers to be efficient in generating data with high expressiveness capacities. Specifically, relational decision trees are learned from sets of tagged subnetworks that provide both classifiers and characteristic patterns for the identified classes.

Query Evaluation under Differential Privacy

Differential privacy has garnered significant attention in recent years due to its potential in offering robust privacy protection for individual data during analysis. With the increasing volume of sensitive information being collected by organizations and analyzed through SQL queries, the development of a general-purpose query engine that is capable of supporting a broad range of queries while maintaining differential privacy has become the holy grail in privacypreserving query release. Towards this goal, this article surveys recent advances in query evaluation under differential privacy.

An efficient and scalable SPARQL query processing framework for big data using MapReduce and hybrid optimum load balancing

The increasing RDF (Resource Description Framework) data volume requires a Hadoop platform for processing queries over large datasets. In this work, SPARQL (Simple Protocol and Rdf Query Language) queries are evaluated with Hadoop based on the objective of minimizing the number of joins through data partitioning for performing map/reduce jobs. The query evaluation time and the number of cross node joins are minimized with the proposed partitioning techniques. Extended vertical partitioning is proposed for distributed data stores based on objects’ explicit information for splitting predicates. For accessing the RDF data, hybrid monarch butterfly with beetle swarm load balancing optimization with Map-reduce (Hybrid Optimum Load Balancing) is applied. The proposed SPARQL query processing is evaluated over large RDF datasets. The proposed approach’s evaluation results are analyzed with the existing approaches, indicating the proposed framework’s efficiency. By using the proposed approach, an accuracy of 97 % is obtained.

GraphOS: Towards Oblivious Graph Processing

We propose GraphOS, a system that allows a client that owns a graph database to outsource it to an untrusted server for storage and querying. It relies on doubly-oblivious primitives and trusted hardware to achieve a very strong privacy and efficiency notion which we call oblivious graph processing : the server learns nothing besides the number of graph vertexes and edges, and for each query its type and response size. At a technical level, GraphOS stores the graph on a doubly-oblivious data structure , so that all vertex/edge accesses are indistinguishable. For this purpose, we propose Omix++, a novel doubly-oblivious map that outperforms the previous state of the art by up to 34×, and may be of independent interest. Moreover, to avoid any leakage from CPU instruction-fetching during query evaluation, we propose algorithms for four fundamental graph queries (BFS/DFS traversal, minimum spanning tree, and single-source shortest paths) that have a fixed execution trace , i.e., the sequence of executed operations is independent of the input. By combining these techniques, we eliminate all information that a hardware adversary observing the memory access pattern within the protected enclave can infer. We benchmarked GraphOS against the best existing solution, based on oblivious relational DBMS (translating graph queries to relational operators). GraphOS is not only significantly more performant (by up to two orders of magnitude for our tested graphs) but it eliminates leakage related to the graph topology that is practically inherent when a relational DBMS is used unless all operations are "padded" to the worst case.

Digital Transformation of the Higher Education System: Directions and Risks

Purpose of research. The higher education system is undergoing changes under the influence of an increasing number of IT solutions used. Transformation changes take place at the organizational, technological, legal, and regulatory levels of management. Each of the directions affects the features of the functioning and development of the higher education system. In the process of their implementation, there are also deviations, risks that need to be, if not eliminated, then at least minimized. The article describes four main directions of development: technical, technological, instrumental, and educational. The types of risks associated with each of the described areas are also highlighted.Materials and methods. A set of methods was used in the paper: bibliographic (selection of articles by keywords); bibliometric (quantitative characteristics by time parameters); content analysis (method of studying the content of articles); evaluation of keyword queries using Internet services.Results. An analysis of queries by keywords showed that interest in the issues of digitalization and digital transformation of higher education arose later than in the system of general secondary education. There is a tendency to adapt successful models of digitalization of secondary education and business areas to the activities of the higher education system. Without considering the peculiarities of the functioning and development of the higher education system, we can get negative consequences expressed in different types of risks. The paper highlights financial, form-major, technological, operational, strategic, cognitive, and social risks.Conclusion. One of the key problems highlighted in the process of analyzing developments in the field of digital transformation of the education system is the consideration of digitalization as means, and not as a catalyst for systemic changes in all areas of activity. Point solutions will not allow you to fully realize the potential of digital solutions. When considering the problems of digitalization and digital transformation, higher education systems are often guided by successful models in the field of secondary general education and / or business environment, which can contribute to the formation of negative consequences when adapting approaches without considering their own specifics.

Digital Transformation of the Higher Education System: Directions and Risks

Purpose of research. The higher education system is undergoing changes under the influence of an increasing number of IT solutions used. Transformation changes take place at the organizational, technological, legal, and regulatory levels of management. Each of the directions affects the features of the functioning and development of the higher education system. In the process of their implementation, there are also deviations, risks that need to be, if not eliminated, then at least minimized. The article describes four main directions of development: technical, technological, instrumental, and educational. The types of risks associated with each of the described areas are also highlighted.Materials and methods. A set of methods was used in the paper: bibliographic (selection of articles by keywords); bibliometric (quantitative characteristics by time parameters); content analysis (method of studying the content of articles); evaluation of keyword queries using Internet services.Results. An analysis of queries by keywords showed that interest in the issues of digitalization and digital transformation of higher education arose later than in the system of general secondary education. There is a tendency to adapt successful models of digitalization of secondary education and business areas to the activities of the higher education system. Without considering the peculiarities of the functioning and development of the higher education system, we can get negative consequences expressed in different types of risks. The paper highlights financial, form-major, technological, operational, strategic, cognitive, and social risks.Conclusion. One of the key problems highlighted in the process of analyzing developments in the field of digital transformation of the education system is the consideration of digitalization as means, and not as a catalyst for systemic changes in all areas of activity. Point solutions will not allow you to fully realize the potential of digital solutions. When considering the problems of digitalization and digital transformation, higher education systems are often guided by successful models in the field of secondary general education and / or business environment, which can contribute to the formation of negative consequences when adapting approaches without considering their own specifics.

Selective Query Processing: A Risk-Sensitive Selection of Search Configurations

In information retrieval systems, search parameters are optimized to ensure high effectiveness based on a set of past searches, and these optimized parameters are then used as the search configuration for all subsequent queries. A better approach, however, would be to adapt the parameters to fit the query at hand. Selective query expansion is one such an approach, in which the system decides automatically whether or not to expand the query, resulting in two possible search configurations. This approach was extended recently to include many other parameters, leading to many possible search configurations where the system automatically selects the best configuration on a per-query basis. One problem with this approach is the system training, which requires evaluation of each training query with every possible configuration. In real-world systems, so many parameters and possible values must be evaluated that this approach is impractical, especially when the system must be updated frequently, as is the case for commercial search engines. In general, the more configurations, the greater the effectiveness when configuration selection is appropriate but also the greater the risk of decreasing effectiveness in the case of an inappropriate configuration selection. To determine the ideal configurations to be used for each query in real-world systems, we have developed a method in which a limited number of possible configurations are pre-selected, then used in a meta-search engine that decides the best search configuration for each query. We define a risk-sensitive approach for configuration pre-selection that considers the risk-reward tradeoff between the number of configurations kept and system effectiveness. We define two alternative risk functions to apply to different goals. For final configuration selection, the decision is based on query feature similarities. We compare two alternative risk functions on two query types (ad hoc and diversity) and compare these to more sophisticated machine learning based methods. We find that a relatively small number of configurations (20) selected by our risk-sensitive model is sufficient to obtain results close to the best achievable results for each query. Effectiveness is increased by about 15% according to the P@10 and nDCG@10 evaluation metrics when compared to traditional grid search using a single configuration and by about 20% when compared to learning to rank documents. Our risk-sensitive approach works for both diversity- and ad hoc oriented searches. Moreover, the similarity-based selection method outperforms the more sophisticated approaches. Thus, we demonstrate the feasibility of developing per-query information retrieval systems, which will guide future research in this direction.

ADDA: An Adversarial Direction-Guided Decision-Based Attack via Multiple Surrogate Models

Over the past decade, Convolutional Neural Networks (CNNs) have been extensively deployed in security-critical areas; however, the security of CNN models is threatened by adversarial attacks. Decision-based adversarial attacks, wherein an attacker relies solely on the final output label of the target model to craft adversarial examples, are the most challenging yet practical adversarial attacks. However, existing decision-based adversarial attacks generally suffer from poor query efficiency or low attack success rate, especially for targeted attacks. To address these issues, we propose a query-efficient Adversarial Direction-guided Decision-based Attack (ADDA), which exploits the advantages of transfer-based priors and the benefits of a single query. The transfer-based priors provided by the gradients of multiple different surrogate models can be utilized to suggest the most promising search directions for generating adversarial examples. The query consumption during the ADDA attack is mainly derived from a single query evaluation of the candidate adversarial samples, which significantly saves the number of queries. Experimental results on several ImageNet classifiers, including l∞ and l2 threat models, demonstrate that our proposed approach overwhelmingly outperforms existing state-of-the-art decision-based attacks in terms of both query efficiency and attack success rate. We show case studies of ADDA against a real-world API in which it is successfully able to fool the Google Cloud Vision API after only a few queries.

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 under single-tuple inserts or deletes to the database. Our approach observes the degree of values in the database and uses different computation and maintenance strategies for high-degree (heavy) and low-degree (light) values. For the latter it partially computes the result, while for the former it computes enough information to allow for on-the-fly enumeration. We define the preprocessing time, the update time, and the enumeration delay as functions of the light/heavy threshold. By appropriately choosing this threshold, our approach recovers a number of prior results when restricted to hierarchical queries. We show that for a restricted class of hierarchical queries, our approach achieves worst-case optimal update time and enumeration delay conditioned on the Online Matrix-Vector Multiplication Conjecture.

Providing in RDBMSs the flexibility to Work with Various Non-Relational Data Models

The inability of pure relational DBMSs to meet the new requirements of the applications that have emerged on the web has led to the advent of No SQL DBMSs. In the last few years, significant progress has been made in integrating into relational DBMSs the features that are essential to consider those new requirements that primarily concern flexibility, performance, horizontal scaling, and very high availability. This paper focuses on the features that can enable relational DBMSs to provide applications with the flexibility to work with various non-relational data models while providing the guarantees of independence, integrity, and performance of query evaluation.

MillenniumDB: An Open-Source Graph Database System

ABSTRACT In this systems paper, we present MillenniumDB: a novel graph database engine that is modular, persistent, and open source. MillenniumDB is based on a graph data model, which we call domain graphs, that provides a simple abstraction upon which a variety of popular graph models can be supported, thus providing a flexible data management engine for diverse types of knowledge graph. The engine itself is founded on a combination of tried and tested techniques from relational data management, state-of-the-art algorithms for worst-case-optimal joins, as well as graph-specific algorithms for evaluating path queries. In this paper, we present the main design principles underlying MillenniumDB, describing the abstract graph model and query semantics supported, the concrete data model and query syntax implemented, as well as the storage, indexing, query planning and query evaluation techniques used. We evaluate MillenniumDB over real-world data and queries from the Wikidata knowledge graph, where we find that it outperforms other popular persistent graph database engines (including both enterprise and open source alternatives) that support similar query features.

Efficient multi-query evaluation for distributed CEP through predicate-based push–pull plans

Complex Event Processing (CEP) evaluates queries over event streams. However, once events are generated by nodes in a network, query evaluation requires the transmission of events between the nodes. Commonly, this is realized by sending events immediately upon their generation. Yet, to reduce network communication, query evaluation may incorporate pull-based communication, where events are buffered locally until they are requested by another node. Existing approaches for push–pull communication in distributed CEP, however, are limited in the expressiveness of pull requests and leverage solely the temporal constraints imposed by a query.In this paper, we propose a new evaluation model for distributed CEP, coined predicate-based push-pull (PrePP) plans. It includes pull requests that enable fine-granular filtering of events at their sources based on query predicates, thereby reducing event transmission. We formulate the problem of constructing PrePP plans with minimal transmission cost that also exploit opportunities for sharing of intermediate results among the queries of workload. However, the construction of optimal PrePP plans turns out to be NP-hard. We therefore propose algorithms to speed up the construction of PrePP plans, while producing near-optimal results. We illustrate the benefits of PrePP plans for distributed CEP in comprehensive experiments with synthetic as well as real-world data. Specifically, PrePP plans can reduce event transmission by up to three orders of magnitude over baseline techniques.

A novel framework for the efficient evaluation of hybrid tree-pattern queries on large data graphs

Graph pattern matching is a fundamental operation for exploring and analyzing large graphs, which are widely used to represent entities and their relationships in a plethora of applications. However, existing algorithms do not perform satisfactorily as they generate a large number of intermediate results.In this paper, we introduce a novel framework to address the problem of efficiently finding homomorphic matches of hybrid tree-patterns over large graphs. These are patterns that can include two types of edges: child edges (which are mapped to edges in the data graph) and/or descendant edges (which are mapped to paths in the data graph), thus allowing for higher expressiveness and flexibility in query formulation. We introduce the concept of answer graph to compactly represent the query results and exploit computation sharing. Leveraging the answer graph, our approach is able to avoid the generation of intermediate results not participating in the query answer, and can produce the query answer in time linear on the size of the input data graph and the output. Moreover, the answer graph allows query counting without explicitly enumerating query results. We design a bottom-up algorithm (BUP) and a simulation-based algorithm (SIM) for building the answer graph, each having their own strengths in different cases. We also present two algorithms for enumerating the results using the answer graph. Our extensive experimental evaluation on real and synthetic datasets shows that our proposed techniques greatly outperform the state-of-the-art graph matching algorithms as well as a popular graph DBMS on evaluating various hybrid tree-pattern queries. Our source code, datasets and queries are publicly available at https://github.com/wuxyng/TwigMatchOnGraph.

Achieving sequenced SQL with log-segmented timestamps

In a period-timestamped, relational temporal database, each tuple is timestamped with a period. The timestamp records when the tuple is “alive” in some temporal dimension. Sequenced semantics is a special semantics for evaluating a query in a temporal database. The semantics stipulates that the query must, in effect, be evaluated simultaneously in each time instant using the tuples alive at that instant. Previous research has proposed changes to a database management system's query evaluation engine to support sequenced semantics. We show how to achieve sequenced semantics without modifying a query evaluation engine. Our technique has two pillars. First we use log-segmented timestamps to record a tuple's lifetime. A log-segmented timestamp divides the time-line into segments of known length. Any temporal period can be represented by a small number of such segments. Second, by taking advantage of the properties of log-segmented timestamps, we translate a sequenced query to a non-temporal query, using the operations already present in an unmodified, non-temporal query evaluation engine. The paper provides translations for both relational algebra and SQL. We experimentally evaluate the SQL translation. The primary contribution of this paper is how to implement sequenced semantics using log-segmented timestamped tuples in a generic SQL DBMS.

MillenniumDB: An Open-Source Graph Database System

Abstract In this systems paper, we present MillenniumDB: a novel graph database engine that is modular, persistent, and open source. MillenniumDB is based on a graph data model, which we call domain graphs, that provides a simple abstraction upon which a variety of popular graph models can be supported, thus providing a flexible data management engine for diverse types of knowledge graph. The engine itself is founded on a combination of tried and tested techniques from relational data management, state-of-the-art algorithms for worst-case-optimal joins, as well as graph-specific algorithms for evaluating path queries. In this paper, we present the main design principles underlying MillenniumDB, describing the abstract graph model and query semantics supported, the concrete data model and query syntax implemented, as well as the storage, indexing, query planning and query evaluation techniques used. We evaluate MillenniumDB over real-world data and queries from the Wikidata knowledge graph, where we find that it outperforms other popular persistent graph database engines (including both enterprise and open source alternatives) that support similar query features.

R2T: Instance-optimal Truncation for Differentially Private Query Evaluation with Foreign Keys

Answering SPJA queries under differential privacy (DP), including graph pattern counting under node-DP as an important special case, has received considerable attention in recent years. The dual challenge of foreign-key constraints and self-joins is particularly tricky to deal with, and no existing DP mechanisms can correctly handle both. For the special case of graph pattern counting under node-DP, the existing mechanisms are correct (i.e., satisfy DP), but they do not offer nontrivial utility guarantees or are very complicated and costly. In this paper, we propose the first DP mechanism for answering arbitrary SPJA queries in a database with foreign-key constraints. Meanwhile, it achieves a fairly strong notion of optimality, which can be considered as a small and natural relaxation of instance optimality. Finally, our mechanism is simple enough that it can be easily implemented on top of any RDBMS and an LP solver. Experimental results show that it offers order-of-magnitude improvements in terms of utility over existing techniques, even those specifically designed for graph pattern counting.

Technical Perspective on 'R2T: Instance-optimal Truncation for Differentially Private Query Evaluation with Foreign Keys

Increased use of data to inform decision making has brought with it a rising awareness of the importance of privacy, and the need for appropriate mitigations to be put in place to protect the interests of individuals whose data is being processed. From the demographic statistics that are produced by national censuses, to the complex predictive models built by "big tech" companies, data is the fuel that powers these applications. A majority of such uses rely on data that is derived from the properties and actions of individual people. This data is therefore considered sensitive, and in need of protections to prevent inappropriate use or disclosure. Some protections come from enforcing policies, access control, and contractual agreements. But in addition, we also seek technical interventions: definitions and algorithms that can be applied by computer systems in order to protect the private information while still enabling the intended use.

Focusing on potential named entities during active label acquisition

AbstractNamed entity recognition (NER) aims to identify mentions of named entities in an unstructured text and classify them into predefined named entity classes. While deep learning-based pre-trained language models help to achieve good predictive performances in NER, many domain-specific NER applications still call for a substantial amount of labeled data. Active learning (AL), a general framework for the label acquisition problem, has been used for NER tasks to minimize the annotation cost without sacrificing model performance. However, the heavily imbalanced class distribution of tokens introduces challenges in designing effective AL querying methods for NER. We propose several AL sentence query evaluation functions that pay more attention to potential positive tokens and evaluate these proposed functions with both sentence-based and token-based cost evaluation strategies. We also propose a better data-driven normalization approach to penalize sentences that are too long or too short. Our experiments on three datasets from different domains reveal that the proposed approach reduces the number of annotated tokens while achieving better or comparable prediction performance with conventional methods.

Optimizing Keyword Search Over Federated RDF Systems

The issue of keyword search in federated resource description framework(RDF) systems is revisited in this paper. Numerous independent SPARQL endpoints that solely offer SPARQL query interfaces make up a federated RDF system. Others find it challenging to remotely build up the global inverted indices over the full RDF network by downloading the dataset at various SPARQL endpoints. This poses a challenge for existing keyword search approaches. In this paper, we propose a keyword search approach for federated RDF systems without building up global inverted indices on the entire RDF graph. We build an offline schema graph for the federated RDF system. During query evaluation, the full-text search interfaces provided by existing SPARQL endpoints were used to map keywords to vertices in the schema graph. Subsequently, the schema graph was explored to construct SPARQL queries from the keywords. The constructed queries were evaluated over the underlying federated RDF systems. Some cost models were proposed to measure keyword mapping and query construction. On the other hand, to further improve the efficiency, a multiple query optimization strategy was designed to rewrite the queries and share the common computation during evaluation. Extensive experiments on real RDF datasets show that the proposed techniques are effective.