Batch Query Research Articles

Atom: An Efficient Query Serving System for Embedding-based Knowledge Graph Reasoning with Operator-level Batching

Knowledge graph reasoning (KGR) answers logical queries over a knowledge graph (KG), and embedding-based KGR (EKGR) becomes popular recently, which embeds both queries and KG entities such that the vector embeddings of a query and its answer entities are similar. Compared with traditional KGR methods based on subgraph matching, EKGR produces fewer intermediate results and is more robust to missing and noisy information in the KG. However, existing systems are inefficient for serving online EKGR queries because they can only batch queries of the same type for execution (i.e., query-level batching ) and hence have limited batching opportunities due to the heterogeneity of queries. To serve EKGR queries efficiently, we propose the Atom system with operator-level batching, which decomposes queries into operators and batches operators of the same type from different queries for execution. The insight is that the types of operators are far fewer than the types of queries, and thus different queries typically share common operators, yielding more batching opportunities. To schedule the operators, Atom adopts a hybrid policy, which improves system throughput and avoids starving rare operators. For efficiency, Atom incorporates system optimizations including two-level pipeline, opportunistic submission, pre-allocated memory buffer, and tailored GPU kernels. Experiment results show that compared with existing systems, Atom can improve query throughput by over 20x and reduce query latency by over 5x. Micro experiments suggest that the designs and optimizations are effective in improving system performance.

Exploiting Shared Sub-Expression and Materialized View Reuse for Multi-Query Optimization

AbstractQuerying in isolation lacks the potential of reusing intermediate results, which ends up wasting computational resources. Multi-Query Optimization (MQO) addresses this challenge by devising a shared execution strategy across queries, with two generally used strategies: batched or cached. These strategies are shown to improve performance, but hardly any study explores the combination of both. In this work we explore such a hybrid MQO, combining batching (Shared Sub-Expression) and caching (Materialized View Reuse) techniques. Our hybrid-MQO system merges batched query results as well as caches the intermediate results, thereby any new query is given a path within the previous plan as well as reusing the results. Since caching is a key component for improving performance, we measure the impact of common caching techniques such as FIFO, LRU, MRU and LFU. Our results show LRU to be the optimal for our usecase, which we use in our subsequent evaluations. To study the influence of batching, we vary the factor - - which represents the similarity of the results within a query batch. Similarly, we vary the cache sizes to study the influence of caching. Moreover, we also study the role of different database operators in the performance of our hybrid system. The results suggest that, depending on the individual operators, our hybrid method gains a speed-up between 4x to a slowdown of 2x from using MQO techniques in isolation. Furthermore, our results show that workloads with a generously sized cache that contain similar queries benefit from using our hybrid method, with an observed speed-up of 2x over sequential execution in the best case.

REMEMProt: a resource of membrane-enriched proteome profiles, their disease associations, and biomarker status.

The differential expression of plasma membrane proteins is integrally analyzed for their diagnosis, prognosis, and therapeutic applications in diverse clinical manifestations. Necessarily, distinct membrane protein enrichment methods and mass spectrometry platforms are employed for their global and relative quantitation. First of its kind to explore, we compiled membrane-associated proteomes in human and mouse systems into a database named, Resource of Experimental Membrane-Enriched Mass spectrometry-derived Proteome (REMEMProt). It currently hosts 14,626 proteins (9,507 proteins in Homo sapiens; 5,119 proteins in Mus musculus) with information on their membrane-protein enrichment methods, experimental/physiological context of detection in cells or tissues, transmembrane domain analysis, and their current attribution as biomarkers. Based on these annotations and the transmembrane domain analysis in proteins or their binary/complex protein-protein interactors, REMEMProt facilitates the assessment of the plasma membrane localization potential of proteins through batch query. A cross-study enrichment analysis platform is enabled in REMEMProt for comparative analysis of proteomes using novel/modified membrane enrichment methods and evaluation of methods for targeted enrichment of membrane proteins. REMEMProt data are made freely accessible to explore and download at https://rememprot.ciods.in/.

HortGenome Search Engine, a universal genomic search engine for horticultural crops.

Horticultural crops comprising fruit, vegetable, ornamental, beverage, medicinal and aromatic plants play essential roles in food security and human health, as well as landscaping. With the advances of sequencing technologies, genomes for hundreds of horticultural crops have been deciphered in recent years, providing a basis for understanding gene functions and regulatory networks and for the improvement of horticultural crops. However, these valuable genomic data are scattered in warehouses with various complex searching and displaying strategies, which increases learning and usage costs and makes comparative and functional genomic analyses across different horticultural crops very challenging. To this end, we have developed a lightweight universal search engine, HortGenome Search Engine (HSE; http://hort.moilab.net), which allows for the querying of genes, functional annotations, protein domains, homologs, and other gene-related functional information of more than 500 horticultural crops. In addition, four commonly used tools, including 'BLAST', 'Batch Query', 'Enrichment analysis', and 'Synteny Viewer' have been developed for efficient mining and analysis of these genomic data.

EMS-i : An Efficient Memory System Design with Specialized Caching Mechanism for Recommendation Inference

Recommendation systems have been widely embedded into many Internet services. For example, Meta’s deep learning recommendation model (DLRM) shows high prefictive accuracy of click-through rate in processing large-scale embedding tables. The SparseLengthSum (SLS) kernel of the DLRM dominates the inference time of the DLRM due to intensive irregular memory accesses to the embedding vectors. Some prior works directly adopt near data processing (NDP) solutions to obtain higher memory bandwidth to accelerate SLS. However, their inferior memory hierarchy induces low performance-cost ratio and fails to fully exploit the data locality. Although some software-managed cache policies were proposed to improve the cache hit rate, the incurred cache miss penalty is unacceptable considering the high overheads of executing the corresponding programs and the communication between the host and the accelerator. To address the issues aforementioned, we propose EMS-i , an efficient memory system design that integrates Solide State Drive (SSD) into the memory hierarchy using Compute Express Link (CXL) for recommendation system inference. We specialize the caching mechanism according to the characteristics of various DLRM workloads and propose a novel prefetching mechanism to further improve the performance. In addition, we delicately design the inference kernel and develop a customized mapping scheme for SLS operation, considering the multi-level parallelism in SLS and the data locality within a batch of queries. Compared to the state-of-the-art NDP solutions, EMS-i achieves up to 10.9× speedup over RecSSD and the performance comparable to RecNMP with 72% energy savings. EMS-i also saves up to 8.7× and 6.6 × memory cost w.r.t. RecSSD and RecNMP, respectively.

NQE: N-ary Query Embedding for Complex Query Answering over Hyper-Relational Knowledge Graphs

Complex query answering (CQA) is an essential task for multi-hop and logical reasoning on knowledge graphs (KGs). Currently, most approaches are limited to queries among binary relational facts and pay less attention to n-ary facts (n≥2) containing more than two entities, which are more prevalent in the real world. Moreover, previous CQA methods can only make predictions for a few given types of queries and cannot be flexibly extended to more complex logical queries, which significantly limits their applications. To overcome these challenges, in this work, we propose a novel N-ary Query Embedding (NQE) model for CQA over hyper-relational knowledge graphs (HKGs), which include massive n-ary facts. The NQE utilizes a dual-heterogeneous Transformer encoder and fuzzy logic theory to satisfy all n-ary FOL queries, including existential quantifiers (∃), conjunction (∧), disjunction (∨), and negation (¬). We also propose a parallel processing algorithm that can train or predict arbitrary n-ary FOL queries in a single batch, regardless of the kind of each query, with good flexibility and extensibility. In addition, we generate a new CQA dataset WD50K-NFOL, including diverse n-ary FOL queries over WD50K. Experimental results on WD50K-NFOL and other standard CQA datasets show that NQE is the state-of-the-art CQA method over HKGs with good generalization capability. Our code and dataset are publicly available.

Efficient and Effective Directed Minimum Spanning Tree Queries

Computing directed Minimum Spanning Tree (DMST) is a fundamental problem in graph theory. It is applied in a wide spectrum of fields from computer network and communication protocol design to revenue maximization in social networks and syntactic parsing in Natural Language Processing. State-of-the-art solutions are online algorithms that compute DMST for a given graph and a root. For multi-query requirements, the online algorithm is inefficient. To overcome the drawbacks, in this paper, we propose an indexed approach that reuses the computation result to facilitate single and batch queries. We store all the potential edges of DMST in a hierarchical tree in O(n) space complexity. Furthermore, we answer the DMST query of any root in O(n) time complexity. Experimental results demonstrate that our approach can achieve a speedup of 2–3 orders of magnitude in query processing compared to the state-of-the-art while consuming O(n) index size.

POInTbrowse: orthology prediction and synteny exploration for paleopolyploid genomes

We describe POInTbrowse, a web portal that gives access to the orthology inferences made for polyploid genomes with POInT, the Polyploidy Orthology Inference Tool. Ancient, or paleo-, polyploidy events are widely distributed across the eukaryotic phylogeny, and the combination of duplicated and lost duplicated genes that these polyploidies produce can confound the identification of orthologous genes between genomes. POInT uses conserved synteny and phylogenetic models to infer orthologous genes between genomes with a shared polyploidy. It also gives confidence estimates for those orthology inferences. POInTbrowse gives both graphical and query-based access to these inferences from 12 different polyploidy events, allowing users to visualize genomic regions produced by polyploidies and perform batch queries for each polyploidy event, downloading genes trees and coding sequences for orthologous genes meeting user-specified criteria. POInTbrowse and the associated data are online at https://wgd.statgen.ncsu.edu.

Deterministic Model of Information Queries Batch Processing in a Multithreaded Server

The analytical model of processing a batch of similar requests in a multithreaded server is considered. The model is based on the high determinism of the duration of individual stages of request processing and the moments of events leading to the switching of the processor from one processing program thread to another. Unlike other deterministic models based on the theory of schedules, in this case the goal is not to find the optimal distribution of work across processors, but to study the dependence of the main characteristics of the software server (packet processing time, marginal performance) on the characteristics of the request and the size of the batch. The model concerns the situa­tion when distributed system with intensive flows of information queries is developed and special measures to ensure the required performance must be taken. One of these measures is the introduction of batch query processing tools, allowing an array of similar information queries to be processed in a single service call. The introduction of such tools usually allows you to repeatedly increase the rate of processing queries in the system and very often becomes a real «lifesaver» for customers who issue, for example, hundreds of thousands of queries per day or more. It is quite likely that a sequence of ordinary single queries cannot provide a solution, and batch processing simply has no alternative

RDF Subgraph Query Based on Common Subgraph in Distributed Environment

With the gradual development of the network, RDF graphs have become more and more complex as the scale of data increases; how to perform more effective query for massive RDF graphs is a hot topic of continuous research. The traditional methods of graph query and graph traversal produce great redundancy of intermediate results, and processing subgraph collection queries in stand-alone mode cannot perform efficient matching when the amount of data is extremely large. Moreover, when processing subgraph collection queries, it is necessary to iterate the query graph multiple times in the query of the common subgraph, and the execution efficiency is not high. In response to the above problems, a distributed query strategy of RDF subgraph set based on composite relation tree is proposed. Firstly, a corresponding composite relationship is established for RDF subgraph set, then the composite relation graph is clipped, and the redundant nodes and edges of the composite relation graph are deleted to obtain the composite relation tree. Finally, using the composite relation tree, a MapReduce-based RDF subgraph set query method is proposed, which can use parallel in the computing environment, the distributed query batch processing is performed on the RDF subgraph set, and the query result of the RDF subgraph set is obtained by traversing the composite relation tree. The experimental results show that the algorithm proposed in this paper can improve the query efficiency of RDF subgraph set.

PIM-Tree

The performance of today's in-memory indexes is bottlenecked by the memory latency/bandwidth wall. Processing-in-memory (PIM) is an emerging approach that potentially mitigates this bottleneck, by enabling low-latency memory access whose aggregate memory bandwidth scales with the number of PIM nodes. There is an inherent tension, however, between minimizing inter-node communication and achieving load balance in PIM systems, in the presence of workload skew. This paper presents PIM-tree , an ordered index for PIM systems that achieves both low communication and high load balance, regardless of the degree of skew in data and queries. Our skew-resistant index is based on a novel division of labor between the host CPU and PIM nodes, which leverages the strengths of each. We introduce push-pull search , which dynamically decides whether to push queries to a PIM-tree node or pull the node's keys back to the CPU based on workload skew. Combined with other PIM-friendly optimizations ( shadow subtrees and chunked skip lists ), our PIM-tree provides high-throughput, (guaranteed) low communication, and (guaranteed) high load balance, for batches of point queries, updates, and range scans. We implement PIM-tree, in addition to prior proposed PIM indexes, on the latest PIM system from UPMEM, with 32 CPU cores and 2048 PIM nodes. On workloads with 500 million keys and batches of 1 million queries, the throughput using PIM-trees is up to 69.7X and 59.1x higher than the two best prior PIM-based methods. As far as we know these are the first implementations of an ordered index on a real PIM system.

VGSC-DB: an online database of voltage-gated sodium channels

As an important member of ion channels family, the voltage-gated sodium channel (VGSC/Nav) is associated with a variety of diseases, including epilepsy, migraine, ataxia, etc., and has always been a hot target for drug design and discovery. Many subtype-selective modulators targeting VGSCs have been reported, and some of them have been approved for clinical applications. However, the drug design resources related to VGSCs are insufficient, especially the lack of accurate and extensive compound data toward VGSCs. To fulfill this demand, we develop the Voltage-gated Sodium Channels Database (VGSC-DB). VGSC-DB is the first open-source database for VGSCs, which provides open access to 6055 data records, including 3396 compounds from 173 references toward nine subtypes of Navs (Nav1.1 ~ Nav1.9). A total of 28 items of information is included in each data record, including the chemical structure, biological activity (IC50/EC50), target, binding site, organism, chemical and physical properties, etc. VGSC-DB collects the data from small-molecule compounds, toxins and various derivatives. Users can search the information of compounds by text or structure, and the advanced search function is also supported to realize batch query. VGSC-DB is freely accessible at http://cadd.zju.edu.cn/vgsc/, and all the data can be downloaded in XLSX/SDF file formats.Graphical

Most relevant point query on road networks.

Graphs are widespread in many real-life practical applications. One of a graph’s fundamental and popular researches is investigating the relations between two given vertices. The relationship between nodes in the graph can be measured by the shortest distance. Moreover, the number of paths is also a popular metric to assess the relationship of different nodes. In many location-based services, users make decisions on the basis of both the two metrics. To address this problem, we propose a new hybrid-metric based on the number of paths with a distance constraint for road networks, which are special graphs. Based on it, a most relevant node query on road networks is identified. To handle this problem, we first propose a Shortest-Distance Constrained DFS, which uses the shortest distance to prune unqualified nodes. To further improve query efficiency, we present Batch Query DFS algorithm, which only needs only one DFS search. Our experiments on four real-life road networks demonstrate the performance of the proposed algorithms.

Reinforcement Learning-Based Sequential Batch-Sampling for Bayesian Optimal Experimental Design

AbstractEngineering problems that are modeled using sophisticated mathematical methods or are characterized by expensive-to-conduct tests or experiments are encumbered with limited budget or finite computational resources. Moreover, practical scenarios in the industry, impose restrictions, based on logistics and preference, on the manner in which the experiments can be conducted. For example, material supply may enable only a handful of experiments in a single-shot or in the case of computational models one may face significant wait-time based on shared computational resources. In such scenarios, one usually resorts to performing experiments in a manner that allows for maximizing one’s state-of-knowledge while satisfying the above-mentioned practical constraints. Sequential design of experiments (SDOE) is a popular suite of methods that have yielded promising results in recent years across different engineering and practical problems. A common strategy that leverages Bayesian formalism is the Bayesian SDOE, which usually works best in the one-step-ahead or myopic scenario of selecting a single experiment at each step of a sequence of experiments. In this work, we aim to extend the SDOE strategy, to query the experiment or computer code at a batch of inputs. To this end, we leverage deep reinforcement learning (RL)-based policy gradient methods, to propose batches of queries that are selected taking into account the entire budget in hand. The algorithm retains the sequential nature, inherent in the SDOE while incorporating elements of reward based on task from the domain of deep RL. A unique capability of the proposed methodology is its ability to be applied to multiple tasks, for example, optimization of a function, once its trained. We demonstrate the performance of the proposed algorithm on a synthetic problem and a challenging high-dimensional engineering problem.

An Active Learning Algorithm Based on the Distribution Principle of Bhattacharyya Distance

Active learning is a method that can actively select examples with much information from a large number of unlabeled samples to query labeled by experts, so as to obtain a high-precision classifier with a small number of samples. Most of the current research uses the basic principles to optimize the classifier at each iteration, but the batch query with the largest amount of information in each round does not represent the overall distribution of the sample, that is, it may fall into partial optimization and ignore the whole, which will may affect or reduce its accuracy. In order to solve this problem, a special distance measurement method—Bhattacharyya Distance—is used in this paper. By using this distance and designing a new set of query decision logic, we can improve the accuracy of the model. Our method embodies the query of the samples with the most representative distribution and the largest amount of information to realize the classification task based on a small number of samples. We perform theoretical proofs and experimental analysis. Finally, we use different data sets and compare them with other classification algorithms to evaluate the performance and efficiency of our algorithm.

Stream Processing of Shortest Path Query in Dynamic Road Networks

Shortest path query in road network is pervasive in various location-based services nowadays. As the business expands, the scalability issue becomes severer and more servers are deployed to cope with it. Moreover, as the traffic condition keeps changing over time, the existing index-based approaches can hardly adapt to the real-life dynamic environment. Therefore, batch shortest path algorithms have been proposed recently to answer a set of queries together using shareable computation. Besides, they can also work in a highly dynamic environment as no index is needed. However, the existing batch algorithms either assume the batch queries are finely decomposed or just process them without differentiation, resulting in poor query efficiency. In this work, we assume the traffic condition is stable over a short period and treat the issued queries within that period as a stream of query sets. Specifically, we first propose three query set decomposition methods to cluster one query set into multiple query subsets: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Zigzag</i> that considers the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1-N</i> shared computation; <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Co-Clustering</i> that considers the source and target's spatial locality; and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Search-Space-Aware</i> that further incorporates search space estimation. After that, we propose two batch algorithms that take advantage of the previously decomposed query sets for efficient query answering: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R2R</i> that finds a set of approximate shortest paths from one region to another with bounded error; and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Local Cache</i> that improves the existing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Global Cache</i> with higher cache hit ratio. Finally, we design three efficient stream processing methods for intra-batch shared computation. The experiments on a large real-world query sets verify the effectiveness and efficiency of our decomposition methods compared with the state-of-the-art batch algorithms.

SimGQ+: Simultaneously evaluating iterative point-to-all and point-to-point graph queries

Graph processing frameworks are typically designed to optimize the evaluation of a single graph query. However, in practice, we often need to respond to multiple graph queries, either from different users or from a single user performing a complex analytics task. Therefore in this paper we develop SimGQ+, a system that optimizes simultaneous evaluation of a group of vertex queries that originate at different source vertices (e.g., multiple shortest path queries originating at different source vertices) and delivers substantial speedups over a conventional framework that evaluates and responds to queries one by one. Our work considers both point-to-all and point-to-point queries. The performance benefits are achieved via batching and sharing. Batching fully utilizes system resources to evaluate a batch of queries and amortizes runtime overheads incurred due to fetching vertices and edge lists, synchronizing threads, and maintaining computation frontiers. Sharing dynamically identifies shared queries that substantially represent subcomputations in the evaluation of different queries in a batch, evaluates the shared queries, and then uses their results to accelerate the evaluation of all queries in the batch. With four input power-law graphs and four graph algorithms SimGQ+ achieves speedups of up to 45.67× with batch sizes of up to 512 queries over the baseline implementation that evaluates the queries one by one using the state of the art Ligra system. Moreover, both batching and sharing contribute substantially to the speedups.

Estimate the Performance of Cloudera Decision Support Queries

Hive and Impala queries are used to process a big amount of data. The overwriting amount of information requires an efficient data processing system. When we deal with a long-term batch query and analysis Hive will be more suitable for this query. Impala is the most powerful system suitable for real-time interactive Structured Query Language (SQL) query which are added a massive parallel processing to Hadoop distributed cluster. The data growth makes a problem with SQL Cluster because the execution processing time is increased. In this paper, a comparison is demonstrated between the performance time of Hive, Impala and SQL on two different data models with different queries chosen to test the performance. The results demonstrate that Impala outperforms Hive and SQL cluster when it comes to analyze data and processing tasks. Using two benchmark datasets, TPC-H and statistical computing, we compare the performance of Hive, Impala, and SQL clusters 2009 Statistical Graphics Data Expo.

Identification of anthelmintic parbendazole as a therapeutic molecule for HNSCC through connectivity map-based drug repositioning

Head and neck squamous cell carcinoma (HNSCC) is one of the most common human cancers; however, its outcome of pharmacotherapy is always very limited. Herein, we performed a batch query in the connectivity map (cMap) based on bioinformatics, queried out 35 compounds with therapeutic potential, and screened out parbendazole as a most promising compound, which had an excellent inhibitory effect on the proliferation of HNSCC cell lines. In addition, tubulin was identified as a primary target of parbendazole, and the direct binding between them was further verified. Parbendazole was further proved as an effective tubulin polymerization inhibitor, which can block the cell cycle, cause apoptosis and prevent cell migration, and it exhibited reasonable therapeutic effect and low toxicity in the in vivo and in vitro anti-tumor evaluation. Our study repositioned an anthelmintic parbendazole to treat HNSCC, which revealed a therapeutic utility and provided a new treatment option for human cancers.

Robust Cardinality: a novel approach for cardinality prediction in SQL queries

BackgroundDatabase Management Systems (DBMSs) use declarative language to execute queries to stored data. The DBMS defines how data will be processed and ultimately retrieved. Therefore, it must choose the best option from the different possibilities based on an estimation process. The optimization process uses estimated cardinalities to make optimization decisions, such as choosing predicate order.MethodsIn this paper, we propose Robust Cardinality, an approach to calculate cardinality estimates of query operations to guide the execution engine of the DBMSs to choose the best possible form or at least avoid the worst one. By using machine learning, instead of the current histogram heuristics, it is possible to improve these estimates; hence, leading to more efficient query execution.ResultsWe perform experimental tests using PostgreSQL, comparing both estimators and a modern technique proposed in the literature. With Robust Cardinality, a lower estimation error of a batch of queries was obtained and PostgreSQL executed these queries more efficiently than when using the default estimator. We observed a 3% reduction in execution time after reducing 4 times the query estimation error.ConclusionsFrom the results, it is possible to conclude that this new approach results in improvements in query processing in DBMSs, especially in the generation of cardinality estimates.