Query Graph Research Articles

SSQ[formula omitted]: A Subgraph-based Semantic Query Approach for Temporal Knowledge Graph

Real-world knowledge graphs are growing in size with the explosion of data and rapid expansion of knowledge. There are some studies on knowledge graph query, but temporal knowledge graph (TKG) query is still a relatively unexplored field. A temporal knowledge graph is a knowledge graph that contains temporal information and contains knowledge that is likely to change over time. It introduces a temporal dimension that can characterize the changes and evolution of entities and relationships at different points in time. However, in the existing temporal knowledge graph query, the entity labels are one-sided, which cannot accurately reflect the semantic relationships of temporal knowledge graphs, resulting in incomplete query results. For the processing of temporal information in temporal knowledge graphs, we propose a temporal frame filtering approach and measure the acceptability of temporal frames by the new definition simtime based on the proposed three temporal frames and nine rules. For measuring the semantic relationship of predicates between entities, we vectorize the semantic similarity between predicates, i.e., edges, using the knowledge embedding model, and propose the new definition simpre to measure the semantic similarity of predicates. Based on these, we propose a new semantic temporal knowledge graph query method SSQTKG, and perform pruning operations to optimize the query efficiency of the algorithm based on connectivity. Extensive experiments show that SSQTKG can return more accurate and complete results that meet the query conditions in the semantic query and can improve the performance of the querying on the temporal knowledge graph.

Using query semantic and feature transfer fusion to enhance cardinality estimating of property graph queries

With the increasing complexity and diversity of query tasks, cardinality estimation has become one of the most challenging problems in query optimization. In this study, we propose an efficient and accurate cardinality estimation method to address the cardinality estimation problem in property graph queries, particularly in response to the current research gap regarding the neglect of contextual semantic features. We first propose formal representations of the property graph query and define its cardinality estimation problem. Then, through the query featurization, we transform the query into a vector representation that can be learned by the estimation model, and enrich the feature vector representation by the context semantic information of the query. We finally propose an estimation model for property graph queries, specifically introducing a feature information transfer module to dynamically control the information flow meanwhile achieving the model’s feature fusion and inference. Experimental results on three datasets show that the estimation model can accurately and efficiently estimate the cardinality of property graph queries, the mean Q_error and RMSE are reduced by about 30% and 25% than the state-of-art estimation models. The context semantics features of queries can improve the model’s estimation accuracy, the mean Q_error result is reduced by about 20% and the RMSE result is about 5%.

Explaining results of path queries on graphs: Single-path results for context-free path queries

Many graph query languages use, at their core, path queries that yield node pairs (m,n) that are connected by a path of interest. For the end-user, such node pairs only give limited insight as to why this result is obtained, as the pair does not directly identify the underlying path of interest.In this paper, we propose the single-path semantics to address this limitation of path queries. Under single-path semantics, path queries evaluate to a single path connecting nodes m and n and that satisfies the conditions of the query. To put our proposal in practice, we provide an efficient algorithm for evaluating context-free path queries using the single-path semantics. Additionally, we perform a short evaluation of our techniques that shows that the single-path semantics is practically feasible, even when query results grow large.In addition, we explore the formal relationship between the single-path semantics we propose the problem of finding the shortest string in the intersection of a regular language (representing a graph) and a context-free language (representing a path query). As our formal results show, there is a distinction between the complexity of the single-path semantics for queries that use a single edge label and queries that use multiple edge labels: for queries that use a single edge label, the length of the shortest path is linearly upper bounded by the number of nodes in the graph; whereas for queries that use multiple edge labels, the length of the shortest path has a worst-case quadratic lower bound.

A framework for integrating biomedical knowledge in Wikidata with open biological and biomedical ontologies and MeSH keywords

This study presents a comprehensive framework to enhance Wikidata as an open and collaborative knowledge graph by integrating Open Biological and Biomedical Ontologies (OBO) and Medical Subject Headings (MeSH) keywords from PubMed publications. The primary data sources include OBO ontologies and MeSH keywords, which were collected and classified using SPARQL queries for RDF knowledge graphs. The semantic alignment between OBO ontologies and Wikidata was evaluated, revealing significant gaps and distorted representations that necessitate both automated and manual interventions for improvement. We employed pointwise mutual information to extract biomedical relations among the 5000 most common MeSH keywords in PubMed, achieving an accuracy of 89.40 % for superclass-based classification and 75.32 % for relation type-based classification. Additionally, Integrated Gradients were utilized to refine the classification by removing irrelevant MeSH qualifiers, enhancing overall efficiency. The framework also explored the use of MeSH keywords to identify PubMed reviews supporting unsupported Wikidata relations, finding that 45.8 % of these relations were not present in PubMed, indicating potential inconsistencies in Wikidata. The contributions of this study include improved methodologies for enriching Wikidata with biomedical information, validated semantic alignments, and efficient classification processes. This work enhances the interoperability and multilingual capabilities of biomedical ontologies and demonstrates the critical role of MeSH keywords in verifying semantic relations, thereby contributing to the robustness and accuracy of collaborative biomedical knowledge graphs.

SGIR-Tree: Integrating R-Tree Spatial Indexing as Subgraphs in Graph Database Management Systems

Efficient spatial query processing in Graph Database Management Systems (GDBMSs) has become increasingly important owing to the prevalence of spatial graph data. However, current GDBMSs lack effective spatial indexing, causing performance issues with complex spatial graph queries. This study proposes a spatial index called Subgraph Integrated R-Tree (SGIR-Tree) for efficient spatial query processing in GDBMSs. The SGIR-Tree integrates the hierarchical R-Tree structure with the graph structure of GDBMSs by converting R-Tree elements into graph components like nodes and edges. The Minimum Bounding Rectangle (MBR) information of spatial objects and R-Tree nodes is stored as properties of these graph elements, and the leaf nodes are directly connected to the spatial nodes. This approach combines the efficiency of spatial indexing with the flexibility of graph databases, thereby allowing spatial query results to be directly utilized in graph traversal. Experiments using OpenStreetMap datasets demonstrate that the SGIR-Tree outperforms the previous approaches in terms of query overhead and index overhead. The results are expected to improve spatial graph data processing in various fields, including location-based service and urban planning, significantly advancing spatial data management in GDBMSs.

Temporal graph processing in modern memory hierarchies

Updates in graph DBMS lead to structural changes in the graph over time with different intermediate states. Capturing these changes and their time is one of the main purposes of temporal DBMS. Most DBMSs built their temporal features based on their non-temporal processing and storage without considering the memory hierarchy of the underlying system. This leads to slower temporal processing and poor storage utilization. In this paper, we propose a storage and processing strategy for (bi-) temporal graphs using temporal materialized views (TMV) while exploiting the memory hierarchy of a modern system. Further, we show a solution to the query containment problem for certain types of temporal graph queries. Finally, we evaluate the overhead and performance of the presented approach. The results show that using TMV reduces the runtime of temporal graph queries while using less memory.

Accurate Sampling-Based Cardinality Estimation for Complex Graph Queries

Accurately estimating the cardinality (i.e., the number of answers) of complex queries plays a central role in database systems. This problem is particularly difficult in graph databases, where queries often involve a large number of joins and self-joins. Recently, Park et al. [ 55 ] surveyed seven state-of-the-art cardinality estimation approaches for graph queries. The results of their extensive empirical evaluation show that a sampling method based on the WanderJoin online aggregation algorithm [ 47 ] consistently offers superior accuracy. We extended the framework by Park et al. [ 55 ] with three additional datasets and repeated their experiments. Our results showed that WanderJoin is indeed very accurate, but it can often take a large number of samples and thus be very slow. Moreover, when queries are complex and data distributions are skewed, it often fails to find valid samples and estimates the cardinality as zero. Finally, complex graph queries often go beyond simple graph matching and involve arbitrary nesting of relational operators such as disjunction, difference, and duplicate elimination. Neither of the methods considered by Park et al. [ 55 ] is applicable to such queries. In this article, we present a novel approach for estimating the cardinality of complex graph queries. Our approach is inspired by WanderJoin, but, unlike all approaches known to us, it can process complex queries with arbitrary operator nesting. Our estimator is strongly consistent, meaning that the average of repeated estimates converges with probability one to the actual cardinality. We present optimisations of the basic algorithm that aim to reduce the chance of producing zero estimates and improve accuracy. We show empirically that our approach is both accurate and quick on complex queries and large datasets. Finally, we discuss how to integrate our approach into a simple dynamic programming query planner, and we confirm empirically that our planner produces high-quality plans that can significantly reduce end-to-end query evaluation times.

Improving Scene Text Retrieval via Stylized Middle Modality

Scene text retrieval addresses the challenge of localizing and searching for all text instances within scene images based on a query text. This cross-modal task has significant applications in various domains, such as intelligent transportation systems and social media analysis. In practice, ensuring consistency of the same content between two modalities is crucial in improving retrieval accuracy. This paper addresses the issue by introducing a stylized middle modality, which fuses the graphical query text with the style of the extracted text proposal. To this end, we propose a stylized middle modality learning (SM \({}^{2}\) L) framework. The proposed stylized middle modality enables the network to jointly enforce constraints on visual feature coherence and text semantic feature consistency in the optimization phase, thereby minimizing the modality gap in the retrieval space. This brings in two major advantages: 1) SM \({}^{2}\) L will pave the way to seamlessly benefit the scene text retrieval and 2) the proposed learning paradigm enables the machine to avoid adding redundant computing resources in the inference phase. Substantial experiments demonstrate that the proposed method outperforms the state-of-the-art retrieval performance considerably.

Prototype-Guided Graph Reasoning Network for Few-Shot Medical Image Segmentation.

Few-shot semantic segmentation (FSS) is of tremendous potential for data-scarce scenarios, particularly in medical segmentation tasks with merely a few labeled data. Most of the existing FSS methods typically distinguish query objects with the guidance of support prototypes. However, the variances in appearance and scale between support and query objects from the same anatomical class are often exceedingly considerable in practical clinical scenarios, thus resulting in undesirable query segmentation masks. To tackle the aforementioned challenge, we propose a novel prototype-guided graph reasoning network (PGRNet) to explicitly explore potential contextual relationships in structured query images. Specifically, a prototype-guided graph reasoning module is proposed to perform information interaction on the query graph under the guidance of support prototypes to fully exploit the structural properties of query images to overcome intra-class variances. Moreover, instead of fixed support prototypes, a dynamic prototype generation mechanism is devised to yield a collection of dynamic support prototypes by mining rich contextual information from support images to further boost the efficiency of information interaction between support and query branches. Equipped with the proposed two components, PGRNet can learn abundant contextual representations for query images and is therefore more resilient to object variations. We validate our method on three publicly available medical segmentation datasets, namely CHAOS-T2, MS-CMRSeg, and Synapse. Experiments indicate that the proposed PGRNet outperforms previous FSS methods by a considerable margin and establishes a new state-of-the-art performance.

SRank: Guiding schema selection in NoSQL document stores

The rise of big data has led to a greater need for applications to change their schema frequently. NoSQL databases provide flexibility in organizing data and offer multiple choices for structuring and storing similar information. While schema flexibility speeds up initial development, choosing schemas wisely is crucial, as they significantly impact performance, affecting data redundancy, navigation cost, data access cost, and maintainability. This paper emphasizes the importance of schema design in NoSQL document stores. It proposes a model to analyze and evaluate different schema alternatives and suggest the best schema out of various schema alternatives. The model is divided into four phases. The model inputs the Entity-Relationship (ER) model and workload queries. In the Transformation Phase, the schema alternatives are initially developed for each ER model, and subsequently, a schema graph is generated for each alternative. Concurrently, workload queries undergo conversion into query graphs. In the Schema Evaluation phase, the Schema Rank (SRank) is calculated for each schema alternative using query metrics derived from the query graphs and path coverage generated from the schema graphs. Finally, in the Output phase, the schema with the highest SRank is recommended as the most suitable choice for the application. The paper includes a case study of a Hotel Reservation System (HRS) to demonstrate the application of the proposed model. It comprehensively evaluates various schema alternatives based on query response time, storage efficiency, scalability, throughput, and latency. The paper validates the SRank computation for schema selection in NoSQL databases through an extensive experimental study. The alignment of SRank values with each schema's performance metrics underscores this ranking system's effectiveness. The SRank simplifies the schema selection process, assisting users in making informed decisions by reducing the time, cost, and effort of identifying the optimal schema for NoSQL document stores.

Matching and Rewriting Rules in Object-Oriented Databases

Graph query languages such as Cypher are widely adopted to match and retrieve data in a graph representation, due to their ability to retrieve and transform information. Even though the most natural way to match and transform information is through rewriting rules, those are scarcely or partially adopted in graph query languages. Their inability to do so has a major impact on the subsequent way the information is structured, as it might then appear more natural to provide major constraints over the data representation to fix the way the information should be represented. On the other hand, recent works are starting to move towards the opposite direction, as the provision of a truly general semistructured model (GSM) allows to both represent all the available data formats (Network-Based, Relational, and Semistructured) as well as support a holistic query language expressing all major queries in such languages. In this paper, we show that the usage of GSM enables the definition of a general rewriting mechanism which can be expressed in current graph query languages only at the cost of adhering the query to the specificity of the underlying data representation. We formalise the proposed query language in terms declarative graph rewriting mechanisms described as a set of production rules L→R while both providing restriction to the characterisation of L, and extending it to support structural graph nesting operations, useful to aggregate similar information around an entry-point of interest. We further achieve our declarative requirements by determining the order in which the data should be rewritten and multiple rules should be applied while ensuring the application of such updates on the GSM database is persisted in subsequent rewriting calls. We discuss how GSM, by fully supporting index-based data representation, allows for a better physical model implementation leveraging the benefits of columnar database storage. Preliminary benchmarks show the scalability of this proposed implementation in comparison with state-of-the-art implementations.

PMGraph: Accelerating Concurrent Graph Queries over Streaming Graphs

There are usually a large number of concurrent graph queries (CGQs) requirements in streaming graphs. However, existing graph processing systems mainly optimize a single graph query in streaming graphs or CGQs in static graphs. They have a large number of redundant computations and expensive memory access overhead, and cannot process CGQs in streaming graphs efficiently. To address these issues, we propose PMGraph , a software-hardware collaborative accelerator for efficient processing of CGQs in streaming graphs. First, PMGraph centers on fine-grained data, selects graph queries that meet the requirements through vertex data, and utilizes the similarity between different graph queries to merge the same vertices they need to process to address the problem of a large amount of repeated access to the same data by different graph queries in CGQs, thereby reducing memory access overhead. Furthermore, it adopts the update strategy that regularizes the processing order of vertices in each graph query according to the order of the vertex dependence chain, consequently effectively reducing redundant computations. Second, we propose a CGQs-oriented scheduling strategy to increase the data overlap when different graph queries are processed, thereby further improving the performance. Finally, PMGraph prefetches the vertex information according to the global active vertex set Frontier of all graph queries, hiding the memory access latency. It also provides prefetching for the same vertices that need to be processed by different graph queries, reducing the memory access overhead. Compared with the state-of-the-art concurrent graph query software systems Kickstarter-C and Tripoline, PMGraph achieves average speedups of 5.57 × and 4.58 ×, respectively. Compared with the state-of-the-art hardware accelerators Minnow, HATS, LCCG and JetStream, PMGraph achieves the speedup of 3.65 ×, 3.41 ×, 1.73 × and 1.38 × on average, respectively. Experimental results show that our proposed PMGraph outperforms the state-of-the-art concurrent graph processing systems and hardware accelerators.

ArcMatch: high-performance subgraph matching for labeled graphs by exploiting edge domains

Consider a large labeled graph (network), denoted the target. Subgraph matching is the problem of finding all instances of a small subgraph, denoted the query, in the target graph. Unlike the majority of existing methods that are restricted to graphs with labels solely on vertices, our proposed approach, named can effectively handle graphs with labels on both vertices and edges. ntroduces an efficient new vertex/edge domain data structure filtering procedure to speed up subgraph queries. The procedure, called path-based reduction, filters initial domains by scanning them for paths up to a specified length that appear in the query graph. Additionally, ncorporates existing techniques like variable ordering and parent selection, as well as adapting the core search process, to take advantage of the information within edge domains. Experiments in real scenarios such as protein–protein interaction graphs, co-authorship networks, and email networks, show that s faster than state-of-the-art systems varying the number of distinct vertex labels over the whole target graph and query sizes.

Galaxybase: A High Performance Native Distributed Graph Database for HTAP

We introduce Galaxybase, a native distributed graph database that addresses the increasing demands for processing large volumes of graph data in diverse industries like finance, manufacturing, and government. Designed to handle the requirements of both transactional and analytical workloads, Galaxybase stands out with its novel data storage and transaction mechanisms. At its core, Galaxybase utilizes a Log-Structured Adjacency List coupled with an Edge Page structure, optimizing read-write operations across a spectrum of tasks such as graph traversals and single edge queries. A notable aspect of Galaxybase is its execution of custom distributed transaction modes tailored for HTAP transactions, allowing for the facilitation of bidirectional and interactive transactions. It ensures data integrity and minimal latency while enabling simultaneous processing of OLTP and OLAP workloads without blocking. Experimental results show that Galaxybase achieves high throughput and low latency in both OLTP and OLAP workloads, across various graph query scenarios and resource conditions. Galaxybase has been deployed in leading banks, education, telecommunication and energy sectors in China, consistently maintaining robust performance for HTAP workloads over the years.

VQFT: A Visual Query Approach Based on Full-Text Search for Knowledge Graphs

Existing knowledge graph query approaches, whether traditional textual query languages or visual query languages, have steep learning curves that are unfriendly for non-expert users. This demonstration presents a Visual Query approach based on Full-Text search for knowledge graphs, called VQFT, which simplifies the process of querying knowledge graphs for users. Inspired by full-text search techniques, VQFT aims to combine the user-friendliness of visual query with the intuitiveness of full-text search , enabling users to query knowledge graphs as straightforward as using a search engine. Faceted full-text indexes, visual query constructor , and an interactive user interface are designed to achieve this goal. User tests and surveys have demonstrated that VQFT is more user-friendly and easier to learn than existing methods, which simplifies the construction of knowledge graph queries for non-expert users.

DTGraph: Declarative Transformations of Property Graphs

Current graph query languages, including the standards SQL/PGQ and GQL, define their semantics in terms of sets of tuples. This is largely inadequate for data interoperability tasks such as data migration or data integration which require queries to output new property graphs. This demonstration showcases DTGraph, an open-source declarative rule-based framework for easily specifying and efficiently executing property graph transformations. We describe a novel comprehensive system that allows the declarative specification of property graph transformations, by extending openCypher queries with a new GENERATE clause for creating new property graphs. The system includes several modules: a parser, a compiler for translating the transformation logic into an efficient executable openCypher script, and an interface assisting users in developing their transformations. The demonstration showcases the ability of our framework to scale to large graph data, and its suitability for transforming real-world datasets.

TransFOL: A Logical Query Model for Complex Relational Reasoning in Drug-Drug Interaction.

Predicting drug-drug interaction (DDI) plays a crucial role in drug recommendation and discovery. However, wet lab methods are prohibitively expensive and time-consuming due to drug interactions. In recent years, deep learning methods have gained widespread use in drug reasoning. Although these methods have demonstrated effectiveness, they can only predict the interaction between a drug pair and do not contain any other information. However, DDI is greatly affected by various other biomedical factors (such as the dose of the drug). As a result, it is challenging to apply them to more complex and meaningful reasoning tasks. Therefore, this study regards DDI as a link prediction problem on knowledge graphs and proposes a DDI prediction model based on Cross-Transformer and Graph Convolutional Networks (GCNs) in first-order logical query form, TransFOL. In the model, a biomedical query graph is first built to learn the embedding representation. Subsequently, an enhancement module is designed to aggregate the semantics of entities and relations. Cross-Transformer is used for encoding to obtain semantic information between nodes, and GCN is used to gather neighbour information further and predict inference results. To evaluate the performance of TransFOL on common DDI tasks, we conduct experiments on two benchmark datasets. The experimental results indicate that our model outperforms state-of-the-art methods on traditional DDI tasks. Additionally, we introduce different biomedical information in the other two experiments to make the settings more realistic. Experimental results verify the strong drug reasoning ability and generalization of TransFOL in complex settings.

“It answers questions that I didn’t know I had”: PhD students’ evaluation of an information-sharing knowledge graph

PurposeInterdisciplinary PhD programs can be challenging as the vital information needed by students may not be readily available; it is scattered across the university’s websites, while tacit knowledge can be obtained only by interacting with people. Hence, there is a need to develop a knowledge management model to create, query and maintain a knowledge repository for interdisciplinary students. This study aims to propose a knowledge graph containing information on critical categories (faculty, classes, dissertations, etc.) and their relationships, extracted from multiple sources, essential for interdisciplinary PhD students. This study evaluates the usability of a participatory-designed knowledge graph intended to facilitate information exchange and decision-making.MethodologyThe authors used data from multiple sources (such as university websites, faculty profiles, publication and dissertation metadata and crowdsourced data) to generate a knowledge graph in the Neo4J Bloom platform. The authors recruited 15 interdisciplinary PhD students using convenience sampling from the University of Hawaiʻi at Mānoa at various PhD stages to design and populate the knowledge graph. Next, the authors conducted a mixed methods study to perform its usability evaluation. First, the authors engaged the students in a participatory design workshop to identify relevant graph queries. Second, the authors conducted semi-structured interviews to determine the usability of the knowledge graph and rate the queries. Each interview was coded with structural and thematic codes and was further analyzed using sentiment analysis in R programming language.FindingsThe usability findings demonstrate that interaction with this knowledge graph benefits PhD students by notably reducing uncertainty and academic stress, particularly among newcomers. Knowledge graph supported them in decision-making, especially when choosing collaborators (e.g. supervisor or dissertation committee members) in an interdisciplinary setting. Key helpful features are related to exploring student–faculty networks, milestones tracking, rapid access to aggregated data and insights into crowdsourced fellow students’ activities. However, they showed concerns about crowdsourced data privacy and accessibility. Although participants expressed the need for more qualitative data in the graph, they noted it helped identify people to talk to about the topics of their interest.OriginalityThe knowledge graph provides a solution to meet the personalized needs of doctoral researchers and has the potential to improve the information discovery and decision-making process substantially. It also includes the tacit knowledge exchange support missing from most current approaches, which is critical for this population and establishing interdisciplinary collaborations. This approach can be applied to other interdisciplinary programs and domains globally.

An Experimental Evaluation of Summarisation-Based Frequent Subgraph Mining for Subgraph Searching

The subgraph searching is a fundamental operation for the analysis and exploration of graphs. Nowadays, molecular databases are nearing close to one hundred million molecules. Since finding all the data graphs in a graph database that contain the query graph using subgraph isomorphism is an NP-complete problem, indexes are built and processed. Further, to assist the formulation of the query by a user, the visual exploratory subgraph query paradigm proposes a graphical user interface and leverages exploration time to reduce query processing time. However, state-of-the-art approaches need to scale better to dynamic graph databases and suffer from efficiency problems. In addition, the existing Summarisation-based frequent subgraph mining for visual exploratory subgraph searching (SuMExplorer) is lacking implementation and evaluation study for handling visual subgraph similarity search and modify operations. In this paper, we present a novel index structure, which aids the subgraph searching using the summarised-based weighted frequent subgraph mining on data graphs. By the structure-preserving, we exploit the indexes to support similarity and modify operations. We conduct extensive performance studies on both real-world and synthetic datasets to evaluate the overall performance of the extended SuMExplorer to the recent visual exploratory FERRARI and traditional subgraph search algorithms (such as the gIndex and the GRAPES-DD). Our results showed that our indexes can query up to 3 times faster in comparison to the FERRARI while reducing the storage footprint by 2 orders of magnitude.

TC-Match: Fast Time-Constrained Continuous Subgraph Matching

Continuously monitoring structural patterns in streaming graphs is a critical task in many real-time graph-based applications. In this paper, we study the problem of time-constrained continuous subgraph matching (shorted as TCSM) over streaming graphs. Given a query graph Q with timing order constraint and a data graph stream G , TCSM aims to report all incremental matches of Q in G for each update of G , where a match should obey both structure constraint (i.e., isomorphism) and timing order constraint of Q. Although TCSM has a wide range of applications, such as cyber-attack detection and credit card fraud detection, we note that this problem has not been well addressed. The state-of-the-art bears the limitations of high index space cost and intermediate result maintenance cost. In this paper, we propose TC-Match, an effective approach to TCSM. First, we design a space and time cost-effective index CSS, which is essentially a k -partite graph structure where a node corresponds to an edge in G. By carefully creating links between nodes, we can encapsulate into CSS the partial embedding and timing order information between edges in G. We theoretically show that CSS has polynomial space and construction time complexities. Second, based on the property of CSS, we develop an efficient incremental matching algorithm with an effective node merging optimization. Extensive experiments show that TC-Match can achieve up to 3 orders of magnitude query performance improvement over the baseline methods, and meanwhile the memory consumption is reduced by 48.7%-86.7%.