Query Routing Research Articles

A wave delay neural network for solving label-constrained shortest route query on time-varying communication networks.

The focus of the research is on the label-constrained time-varying shortest route query problem on time-varying communication networks. To the best of our knowledge, research on this issue is still relatively limited, and similar studies have the drawbacks of low solution accuracy and slow computational speed. In this study, a wave delay neural network (WDNN) framework and corresponding algorithms is proposed to effectively solve the label-constrained time-varying shortest routing query problem. This framework accurately simulates the time-varying characteristics of the network without any training requirements. WDNN adopts a new type of wave neuron, which is independently designed and all neurons are parallelly computed on WDNN. This algorithm determines the shortest route based on the waves received by the destination neuron (node). Furthermore, the time complexity and correctness of the proposed algorithm were analyzed in detail in this study, and the performance of the algorithm was analyzed in depth by comparing it with existing algorithms on randomly generated and real networks. The research results indicate that the proposed algorithm outperforms current existing algorithms in terms of response speed and computational accuracy.

Data Communication Algorithm of HPDB Parallel Database System Based on Computer Network

High-Performance Database (HPDB) is designed to meet the demands of today’s modern data-driven world, as massive volumes of information need to be accessed and analyzed with minimal latency. A pivotal aspect of their operation lies in efficient data communication among nodes in a computer network, which is essential for parallel database systems. HPDBs may involve distributed architectures and parallel database systems to store and process data across multiple nodes or servers in a network. Hence, an algorithm called Hybridized Partitioning Strategy (HPS)-based Communication (C) for achieving HPDB has been proposed to facilitate data transmission and coordination across a computer network using the Message Passing Interface (MPI) protocol. The proposed HPS-C-HPDB technique includes partitioning and distributing data, query routing, and load balancing strategies to achieve high-performance levels. The (HPS) combines hash and range partitioning methods for effective processing and retrieval to balance data distribution, reduce communication overhead in parallel databases, and improve the system’s performance. In parallel database systems, query routing effectively routes requests into the optimal nodes or partitions based on the query’s conditions and the data’s placement and guarantees efficient data processing and retrieval. The proposed scheme is evaluated using various performance metrics like throughput, response time, speedup, and communication overhead analysis.

Grid-Based Non-Uniform Probabilistic Roadmap-Based AGV Path Planning in Narrow Passages and Complex Environments

In this paper, we propose a Grid-based Non-uniform Probability Density Sampling Probabilistic Roadmap algorithm (GN-PRM) in response to the challenges of difficult sampling in narrow passages and low-probability map generation in traditional Probabilistic Roadmap algorithms (PRM). The improved algorithm incorporates grid-based processing for map segmentation, employing non-uniform probability density sampling based on the different attributes of each block to enhance sampling probability in narrow passages. Additionally, considering the computational cost and frequent ineffective searches in traditional PRM algorithms during pathfinding, this paper optimizes the time required for query route planning by altering connection strategies to improve the algorithm’s runtime. Finally, the simulation results indicate that, with a reduction of over 50% in undirected line segments and a reduction of over 85% in runtime, the GN-PRM algorithm achieves a 100% success rate in complex planning scenarios with a sampling point value of K = 500. In comparison, the traditional PRM algorithm has a success rate of no more than 10%, with a sampling point value of K = 500.

A robust hybrid model with low energy consumption for biosensor nano-networks

The biosensor nano-network consists of mobile nano-nodes, nano-routers and micro-gateways in various levels of hierarchy. In the human body, the energy is harvested by the nano-devices by transforming various forms of energy into electrical energy. Simultaneously, the energy is consumed to collect and transmit data to the external network. So, a proper balance between energy harvesting and energy consumption is required. The current multi-path routing enables data flooding, using energy from numerous redundant nodes along the path. Whereas, single-path routing has the drawback of packet loss. In this article, we propose a hybrid model with low energy consumption and minimal packet loss. We grouped the nano-nodes into clusters by which the redundancy is avoided to a great extent. The nano-nodes communicate with a cluster primary node using a Lightweight Secure Directed Diffusion multi-path protocol, and the primary nodes communicate with the nano-router using Energy Conserving Routing single-path protocol at the layer-1 of our model. In the conventional models, the micro-gateway communication with the nano-routers created energy irregularities. So, we come up with a query routing protocol at the layer-2 that chooses a particular nano-router for communication with the micro-gateway. As a result, the energy consumption at the micro-gateway is decreased.

Check Out the Big Brain on BRAD: Simplifying Cloud Data Processing with Learned Automated Data Meshes

The last decade of database research has led to the prevalence of specialized systems for different workloads. Consequently, organizations often rely on a combination of specialized systems, organized in a Data Mesh. Data meshes present significant challenges for system administrators, including picking the right system for each workload, moving data between systems, maintaining consistency, and correctly configuring each system. Many non-expert end users (e.g., data analysts or app developers) either cannot solve their business problems, or suffer from sub-optimal performance or cost due to this complexity. We envision BRAD, a cloud system that automatically integrates and manages data and systems into an instance-optimized data mesh, allowing users to efficiently store and query data under a unified data model (i.e., relational tables) without knowledge of underlying system details. With machine learning, BRAD automatically deduces the strengths and weaknesses of each engine through a combination of offline training and online probing. Then, BRAD uses these insights to route queries to the most suitable (combination of) system(s) for efficient execution. Furthermore, BRAD automates configuration tuning, resource scaling, and data migration across component systems, and makes recommendations for more impactful decisions, such as adding or removing systems. As such, BRAD exemplifies a new class of systems that utilize machine learning and the cloud to make complex data processing more accessible to end users, raising numerous new problems in database systems, machine learning, and the cloud.

Personalized Route Recommendation With Neural Network Enhanced Search Algorithm

In this work, we study an important task in location-based services, namely <i>Personalized Route Recommendation (PRR)</i> . Given a road network, the PRR task aims to generate user-specific route suggestions for replying to users’ route queries. A classic approach is to adapt search algorithms to construct pathfinding-like solutions. These methods typically focus on reducing search space with suitable heuristic strategies. For these search algorithms, heuristic strategies are often handcrafted, which are not flexible to work in complicated task settings. In addition, it is difficult to utilize useful context information in the search procedure. To develop a more principled solution to the PRR task, we propose to improve search algorithms with neural networks for solving the PRR task based on the widely used <inline-formula><tex-math notation="LaTeX">$A^{*}$</tex-math></inline-formula> algorithm. The main idea of our solution is to automatically learn the cost functions in <inline-formula><tex-math notation="LaTeX">$A^{*}$</tex-math></inline-formula> algorithms, which is the key of heuristic search algorithms. Our model consists of two main components. First, we employ attention-based Recurrent Neural Networks (RNN) to model the cost from the source to the candidate location by incorporating useful context information. Instead of learning a single cost value, the RNN component is able to learn a time-varying vectorized representation for the moving state of a user. Second, we propose to use an estimation network for predicting the cost from a candidate location to the destination. For capturing structural characteristics, the estimation network is built on top of position-aware graph attention networks. The two components are integrated in a principled way for deriving a more accurate cost of a candidate location for the <inline-formula><tex-math notation="LaTeX">$A^{*}$</tex-math></inline-formula> algorithm. Extensive experiment results on three real-world datasets have shown the effectiveness and robustness of the proposed model.

A segmented parallel expansion algorithm for keyword-aware optimal route query

A segmented parallel expansion algorithm for keyword-aware optimal route query

LaMeta: An efficient locality aware metadata management technique for an ultra-large distributed storage system

The locality of metadata is a critical parameter of data storage and retrieval performance for an ultra-large distributed storage system. The data of an ultra-large distributed storage system is accessed through its distributed metadata servers (MDSs). The distributed MDSs store and cache partial metadata for scalability and performance. The MDSs are connected through varied latencies multipath network connections of using various metadata distribution techniques. The locality of MDS in the metadata distribution techniques improves the metadata query routing and enhances the performance of the distributed storage system. Motivated by this insight, this work introduces an intelligent locality-aware metadata management technique based on recommendation system that exploits MDS path ratings in a globally distributed ultra-large storage system and proposes a novel locality-aware metadata query routing algorithm. The proposed metadata server locality aware employs collaborative filtering with Stacked AutoEncoder to address the sparsity of the path rating matrix and its overfitting issue. The extensive experiments on real datasets show that the proposed metadata server locality aware technique enhances the locality of metadata operations and improves aggregate operations throughput by 12% to 21% of contemporary distributed metadata management techniques in ultra-large distributed storage systems.

Top k Optimal Sequenced Route Query with POI Preferences

The optimal sequenced route (OSR) query, as a popular problem in route planning for smart cities, searches for a minimum-distance route passing through several POIs in a specific order from a starting position. In reality, POIs are usually rated, which helps users in making decisions. Existing OSR queries neglect the fact that the POIs in the same category could have different scores, which may affect users’ route choices. In this paper, we study a novel variant of OSR query, namely Rating Constrained Optimal Sequenced Route query (RCOSR), in which the rating score of each POI in the optimal sequenced route should exceed the query threshold. To efficiently process RCOSR queries, we first extend the existing TD-OSR algorithm to propose a baseline method, called MTDOSR. To tackle the shortcomings of MTDOSR, we try to design a new RCOSR algorithm, namely Optimal Subroute Expansion (OSE) Algorithm. To enhance the OSE algorithm, we propose a Reference Node Inverted Index (RNII) to accelerate the distance computation of POI pairs in OSE and quickly retrieve the POIs of each category. To make full use of the OSE and RNII, we further propose a new efficient RCOSR algorithm, called Recurrent Optimal Subroute Expansion (ROSE), which recurrently utilizes OSE to compute the current optimal route as the guiding path and update the distance of POI pairs to guide the expansion. Then, we extend our techniques to handle a variation of RCOSR query, namely RCkOSR query. The experimental results demonstrate that the proposed algorithm significantly outperforms the existing approaches.

Deep Q-Learning Based Optimal Query Routing Approach for Unstructured P2P Network

Deep Reinforcement Learning (DRL) is a class of Machine Learning (ML) that combines Deep Learning with Reinforcement Learning and provides a framework by which a system can learn from its previous actions in an environment to select its efforts in the future efficiently. DRL has been used in many application fields, including games, robots, networks, etc. for creating autonomous systems that improve themselves with experience. It is well acknowledged that DRL is well suited to solve optimization problems in distributed systems in general and network routing especially. Therefore, a novel query routing approach called Deep Reinforcement Learning based Route Selection (DRLRS) is proposed for unstructured P2P networks based on a Deep Q-Learning algorithm. The main objective of this approach is to achieve better retrieval effectiveness with reduced searching cost by less number of connected peers, exchanged messages, and reduced time. The simulation results shows a significantly improve searching a resource with compression to k-Random Walker and Directed BFS. Here, retrieval effectiveness, search cost in terms of connected peers, and average overhead are 1.28, 106, 149, respectively.

Intelligent neighbor selection for efficient query routing in unstructured P2P networks using Q-learning

Intelligent neighbor selection for efficient query routing in unstructured P2P networks using Q-learning

Standardizing Confidentiality Protections for Domain Name System Exchanges: Multiple Approaches, New Functionality

Confidentiality protections have become a major focus of standards development for the Domain Name System (DNS) protocol. DNS encryption techniques as well as alternative techniques with lower operational impact have both emerged. This article provides a high-level overview of these techniques and the considerations for applying them in various parts of the DNS ecosystem. The article also discusses how the standardization of DNS encryption can facilitate a new multi-resolver architecture where clients route queries to one or more special-purpose resolvers associated with enterprise or application namespaces.

A Routing Query Algorithm Based on Time-Varying Relationship Group in Opportunistic Social Networks

With the fast development of IoT and 5G technologies, opportunity social networks composed of portable mobile devices have become a hot research topic in recent years. However, arbitrary node movement in opportunity networks and the absence of end-to-end pathways make node communication unstable. At the same time, the problem of ignoring human social preferences and relying on wrong message relay nodes lead to a low data transmission rate and high network overhead. Based on the above issues, we propose a time-varying relationship groups-based routing query algorithm for mobile opportunity networks (Time-varying Relationship Groups, TVRGs). Firstly, we construct the relationship groups based on the time-varying characteristics according to the intimacy between users. Secondly, we calculate the importance of nodes by their connectivity time and communication frequency. Finally, we find the suitable message relay nodes according to the similarity of node weights and their action trajectories and design the routing query algorithm accordingly. The simulation results show that the algorithm can vastly improve the message query success rate, effectively improve the data transmission efficiency, and reduce the average delay and system overhead compared with the existing routing algorithms.

Querying Optimal Routes for Group Meetup

Motivated by location-based social networks which allow people to access location-based services as a group, we study a novel variant of optimal sequenced route (OSR) queries, optimal sequenced route for group meetup (OSR-G) queries. OSR-G query aims to find the optimal meeting POI (point of interest) such that the maximum users’ route distance to the meeting POI is minimized after each user visits a number of POIs of specific categories (e.g., gas stations, restaurants, and shopping malls) in a particular order. To process OSR-G queries, we first propose an OSR-Based (OSRB) algorithm as our baseline, which examines every POI in the meeting category and utilizes existing OSR (called E-OSR) algorithm to compute the optimal route for each user to the meeting POI. To address the shortcomings (i.e., requiring to examine every POI in the meeting category) of OSRB, we propose an upper bound based filtering algorithm, called circle filtering (CF) algorithm, which exploits the circle property to filter the unpromising meeting POIs. In addition, we propose a lower bound based pruning (LBP) algorithm, namely LBP-SP which exploits a shortest path lower bound to prune the unqualified meeting POIs to reduce the search space. Furthermore, we develop an approximate algorithm, namely APS, to accelerate OSR-G queries with a good approximation ratio. Finally the experimental results show that both CF and LBP-SP outperform the OSRB algorithm and have high pruning rates. Moreover, the proposed approximate algorithm runs faster than the exact OSR-G algorithms and has a good approximation ratio.

Keyword Query Routing

&#x0D; &#x0D; &#x0D; &#x0D; Abstract&#x0D; An innovative method of accessing connected data sources on the internet is keyword search. We propose to route keywords only to relevant sources in order to reduce the substantial processing cost of keyword search queries throughout all providers. We propose a novel method for computing top-k routing plans by considering their propensity to contain responses for a specific keyword query. We use a summary of the links between the data components referencing certain keywords to concisely convey these interactions. For calculating the relevance of routing plans through scores at the level of keywords, data components, element sets, along with sub graphs connecting these elements, a multilayer scoring system is given. We mostly employ two strategies while looking for keywords. Both schema-based and schema-agnostic techniques are involved. On top of commercial databases are developed schema-based techniques. By mapping keywords to the components of databases, also known as keyword elements, a keyword is processed. The calculated keyword components are then joined using the schema-derived valid join sequences to create candidate networks that represent potential answers to the keyword query. Schema-neutral methods work directly with the data. In these methods, the structured outcomes are computed by examining the underlying graphs. Steiner trees and graphs are used to represent keywords and related items. This method seeks to identify structures in the Steiner trees. A Steiner graph, for example, is the path between uni1 and award for the query "Stanley Robert Award". For the effective exploration of keyword search results over data graphs—which may be quite large—a variety of techniques have been developed. Bidirectional search and dynamic programming are two examples.&#x0D; &#x0D; &#x0D; &#x0D;

Where To: Crowd-Aided Path Selection by Selective Bayesian Network

in this paper, we have made the first contribution by designing two right types of questions, namely Routing Query (RQ) to ask the crowd to decide the direction at each road intersection. Secondly, we propose a series of efficient algorithms to dynamically manage the questions in order to reduce the selection hardness within a limited budget. In particular, we show that there are two factors affecting the informativeness of a question: the randomness (entropy) of the question and the structural position of the road intersection. Furthermore, we extend the framework to enable multiple RQs per round. To ease the pain of the sample sensitiveness, we propose a new approach to reduce the selection hardness by reasoning on a so-called Selective Bayesian network. We compare our approach against several baselines, and the effectiveness and efficiency of our proposal are verified by the results in simulations and experiments on real-world datasets. The experimental results show that, even the Selective Bayesian Network provides only partial information of causality, the performance on the reduction of the selection hardness are dramatically improved, especially when the size of samples are relatively small.

Visualization Environment for Federated Knowledge Graphs: Development of an Interactive Biomedical Query Language and Web Application Interface.

BackgroundEfforts are underway to semantically integrate large biomedical knowledge graphs using common upper-level ontologies to federate graph-oriented application programming interfaces (APIs) to the data. However, federation poses several challenges, including query routing to appropriate knowledge sources, generation and evaluation of answer subsets, semantic merger of those answer subsets, and visualization and exploration of results.ObjectiveWe aimed to develop an interactive environment for query, visualization, and deep exploration of federated knowledge graphs.MethodsWe developed a biomedical query language and web application interphase—termed as Translator Query Language (TranQL)—to query semantically federated knowledge graphs and explore query results. TranQL uses the Biolink data model as an upper-level biomedical ontology and an API standard that has been adopted by the Biomedical Data Translator Consortium to specify a protocol for expressing a query as a graph of Biolink data elements compiled from statements in the TranQL query language. Queries are mapped to federated knowledge sources, and answers are merged into a knowledge graph, with mappings between the knowledge graph and specific elements of the query. The TranQL interactive web application includes a user interface to support user exploration of the federated knowledge graph.ResultsWe developed 2 real-world use cases to validate TranQL and address biomedical questions of relevance to translational science. The use cases posed questions that traversed 2 federated Translator API endpoints: Integrated Clinical and Environmental Exposures Service (ICEES) and Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways (ROBOKOP). ICEES provides open access to observational clinical and environmental data, and ROBOKOP provides access to linked biomedical entities, such as “gene,” “chemical substance,” and “disease,” that are derived largely from curated public data sources. We successfully posed queries to TranQL that traversed these endpoints and retrieved answers that we visualized and evaluated.ConclusionsTranQL can be used to ask questions of relevance to translational science, rapidly obtain answers that require assertions from a federation of knowledge sources, and provide valuable insights for translational research and clinical practice.

ROAM: A Fundamental Routing Query on Road Networks with Efficiency

Novel road-network applications often recommend a moving object (e.g., a vehicle) about interesting services or tasks on its way to a destination. A taxi-sharing system, for instance, suggests a new passenger to a taxi while it is serving another one. The traveling cost is then shared among these passengers. A fundamental query is: given two nodes $s$ s and $t$ t , and an area $\mathcal {A}$ A on road network graph $\mathcal {G}$ G , is there a “good” route (e.g., short enough path) $P$ P from $s$ s to $t$ t that crosses $\mathcal {A}$ A in $\mathcal {G}$ G ? In a taxi-sharing system, $s$ s and $t$ t can be a taxi's current and destined locations, and $\mathcal {A}$ A contains all the places to which a person waiting for a taxi is willing to walk. Answering this Route and Area Matching (ROAM) Query allows the application involved to recommend appropriate services to users efficiently. In this paper, we examine efficient ROAM query algorithms. Particularly, we develop solutions for finding a $\rho$ ρ -route , which is an $s$ s - $t$ t path that passes $\mathcal {A}$ A , with a length of at most $(1+\rho)$ ( 1 + ρ ) times the shortest distance between $s$ s and $t$ t . The existence of a $\rho$ ρ -route implies that a service or task located at $\mathcal {A}$ A can be found for a given moving object $m$ m , and that $m$ m only deviates slightly from its current route. We present comprehensive studies on index-free and index-based algorithms for answering ROAM queries. Comprehensive experiments show that our algorithm runs up to 30 times faster than baseline algorithms.

Incorporating Multi-Source Urban Data for Personalized and Context-Aware Multi-Modal Transportation Recommendation

Transportation recommendation is one important map service in navigation applications. Previous transportation recommendation solutions fail to deliver satisfactory user experience because their recommendations only consider routes in one transportation mode (uni-modal, e.g., taxi, bus, cycle) and largely overlook situational context. In this work, we propose <inline-formula><tex-math notation="LaTeX">$\mathsf {Hydra}$</tex-math></inline-formula> , a multi-task deep learning based recommendation system that offers multi-modal transportation planning and is adaptive to various situational context (e.g., nearby point-of-interest (POI) distribution and weather). We leverage the availability of existing routing engines and big urban data, and design a novel two-level framework that integrates uni-modal and multi-modal (e.g., taxi-bus, bus-cycle) routes as well as heterogeneous urban data for intelligent multi-modal transportation recommendation. In addition to urban context features constructed from multi-source urban data, we learn the latent representations of users, origin-destination (OD) pairs and transportation modes based on user implicit feedbacks, which captures the collaborative transportation mode preferences of users and OD pairs. Moreover, we propose two models to recommend the proper route among various uni-modal and multi-modal transportation routes: (1) a light-weight gradient boosting decision tree (GBDT) based recommendation model; and (2) a multi-task wide and deep learning (MTWDL) based recommendation model. We also optimize the framework to support real-time, large-scale route query and recommendation. We deploy <inline-formula><tex-math notation="LaTeX">$\mathsf {Hydra}$</tex-math></inline-formula> on Baidu Maps, <xref ref-type="fn" rid="fn1">¹</xref> <fn id="fn1"><label>1.</label> <uri>https://maps.baidu.com/</uri>. </fn> one of the world's largest map services. Real-world urban-scale experiments demonstrate the effectiveness and efficiency of our proposed system. Since its deployment in August 2018, <inline-formula><tex-math notation="LaTeX">$\mathsf {Hydra}$</tex-math></inline-formula> has answered over a hundred million route recommendation queries made by over ten million distinct users. The GBDT based model and MTWDL based model achieve 82.8 and 96.6 percent relative improvement of user click ratio, respectively.

Reinforcement Learning Based Query Routing Approach for P2P Systems

Peer-to-peer (P2P) systems have offered users an efficient way to share various resources and access diverse services over the Internet. In unstructured P2P systems, resource storage and indexation are fully distributed among participating peers. Therefore, locating peers sharing pertinent resources for a specific user query is a challenging issue. In fact, effective query routing requires smart decisions to select a certain number of peers with respect to their relevance for the query instead of choosing them at random. In this respect, we introduce here a new query-oriented approach, called the reinforcement learning-based query routing approach (RLQR). The main goal of RLQR is to reach high retrieval effectiveness as well as a lower search cost by reducing the number of exchanged messages and contacted peers. To achieve this, the RLQR relies on information gathered from previously sent queries to identify relevant peers for forthcoming queries. Indeed, we formulate the query routing issue as the reinforcement learning problem and introduce a fully distributed approach for addressing it. In addition, RLQR addresses the well-known cold-start issue during the training stage, which allows it to improve its retrieval effectiveness and search cost continuously, and, therefore, goes quickly through the cold-start phase. Performed simulations demonstrate that RLQR outperforms pioneering query routing approaches in terms of retrieval effectiveness and communications cost.