Since solving State-of-the-art algorithms for Approximate Nearest Neighbor Search the problem exactly requires an expensive exhaustive scan of the (ANNS) such as DiskANN, FAISS-IVF, and HNSW build data dependent database - which would be impractical for real-world indices that indices that offer substantially better accuracy and search span billions of objects - practical interactive search systems use efficiency over data-agnostic indices by overfitting to the index Approximate Nearest Neighbor Search (ANNS) algorithms with data distribution. When the query data is drawn from a different highly sub-linear query complexity [10, 18, 24, 30] to answer such distribution - e.g., when index represents image embeddings and queries. The quality of such ANN indices is often measured by query represents textual embeddings - such algorithms lose much k-recall@k which is the overlap between the top-results of the of this performance advantage. On a variety of datasets, for a fixed index search with the ground truth -nearest neighbors (-NNs) in recall target, latency is worse by an order of magnitude or more for the corpus for the query, averaged over a representative query set. Out-Of-Distribution (OOD) queries as compared to In-Distribution State-of-the-art algorithms for ANNS, such as graph-based indices (ID) queries. The question we address in this work is whether ANNS [16, 24, 30] which use data-dependent index construction, algorithms can be made efficient for OOD queries if the index construction achieve better query efficiency over prior data-agnostic methods is given access to a small sample set of these queries. We like LSH [6, 18] (see Section A.1 for more details). Such efficiency answer positively by presenting OOD-DiskANN, which uses a sparing enables these indices to serve queries with > 90% recall with a sample (1% of index set size) of OOD queries, and provides up to latency of a few milliseconds, required in interactive web scenarios.

This paper introduces DIAMETRICS: a novel framework for end-to-end benchmarking and performance monitoring of query engines. DIAMETRICS consists of a number of components supporting tasks such as automated workload summarization, data anonymization, benchmark execution, monitoring, regression identification, and alerting. The architecture of DIAMETRICS is highly modular and supports multiple systems by abstracting their implementation details and relying on common canonical formats and pluggable software drivers. The end result is a powerful unified framework that is capable of supporting every aspect of benchmarking production systems and workloads. DIAMETRICS has been developed in Google and is being used to benchmark various internal query engines. In this paper, we give an overview of DIAMETRICS and discuss its design and implementation. Furthermore, we provide details about its deployment and example use cases. Given the variety of supported systems and use cases within Google, we argue that its core concepts can be used more widely to enable comparative end-to-end benchmarking in other industrial environments. The data management landscape has drastically changed over the last few years. The majority of database systems are no longer manually tuned and optimized for a specific application by well-versed administrators; instead, they are designed to support a variety of applications. To support all of these applications, a multitude of data models, storage formats, and query engines have transformed the data management landscape from standalone, specialized deployments to entire ecosystems.

You are preeminent in your field, a singular talent. Using cleverness and craft you imagine factory designs that are elegant and streamlined. No inch of space wasted, not an inefficiency in sight. Imagine you want to design a megafactory - a factory that designs factories - encapsulating everything you know into one automated machine. You embark on an impossible journey.

However, a key limitation is that their DSLs can only search for events that are located within a close subgraph neighborhood. System auditing has emerged as a key approach for monitoring Thus, these approaches cannot efficiently reveal faraway system call events and investigating sophisticated attacks. Based on events on a long-range attack sequence, which is observed in many the collected audit logs, research has proposed to search for attack of the attacks these days due to their sophisticated, multi-stage patterns or track the causal dependencies of system events to reveal nature [55]. Tracking-based approaches assume causal dependencies the attack sequence. However, existing approaches either cannot between system entities that are involved in the same system reveal long-range attack sequences or suffer from the dependency event (e.g., a process reading a file) [45, 48, 52, 54]. Based on this explosion problem due to a lack of focus on attack-relevant parts, assumption, these approaches track the dependencies from a Point and thus are insufficient for investigating complex attacks. of Interest (POI) event (e.g., an alert event like the creation of a To bridge the gap, we propose Zebra, a system that synergistically suspicious file) and construct a system dependency graph, in which integrates attack pattern search and causal dependency tracking nodes represent system entities and edges represent system events.

Extractive question answering (ExQA) is an essential task for Natural Language Processing. The dominant approach to ExQA is one that represents the input sequence tokens (question and passage) with a pre-trained transformer, then uses two learned query vectors to compute distributions over the start and end answer span positions. These query vectors lack the context of the inputs, which can be a bottleneck for the model performance. To address this problem, we propose \textit{DyREx}, a generalization of the \textit{vanilla} approach where we dynamically compute query vectors given the input, using an attention mechanism through transformer layers. Empirical observations demonstrate that our approach consistently improves the performance over the standard one. The code and accompanying files for running the experiments are available at \url{https://github.com/urchade/DyReX}.

Recent advances have shown the success of using reinforcement learning and search to solve NP-hard graph-related tasks, such as Traveling Salesman Optimization, Graph Edit Distance computation, etc. However, it remains unclear how one can efficiently and accurately detect the occurrences of a small query graph in a large target graph, which is a core operation in graph database search, biomedical analysis, social group finding, etc. This task is called Subgraph Matching which essentially performs subgraph isomorphism check between a query graph and a large target graph. One promising approach to this classical problem is the "learning-to-search" paradigm, where a reinforcement learning (RL) agent is designed with a learned policy to guide a search algorithm to quickly find the solution without any solved instances for supervision. However, for the specific task of Subgraph Matching, though the query graph is usually small given by the user as input, the target graph is often orders-of-magnitude larger. It poses challenges to the neural network design and can lead to solution and reward sparsity. S with two innovations to tackle the challenges: (1) A novel encoder-decoder neural network architecture to dynamically compute the matching information between the query and the target graphs at each search state; (2) A novel look-ahead loss function for training the policy network. S can significantly improve the subgraph matching performance. With the growing amount of graph data that naturally arises in many domains, solving graph-related tasks via machine learning has gained increasing attention.

Graph representations for real-world social networks in the past have missed two important elements: the multiplexity of connections as well as representing time. To this end, in this paper, we present a new dynamic heterogeneous graph representation for social networks which includes time in every single component of the graph, i.e., nodes and edges, each of different types that captures heterogeneity. We illustrate the power of this representation by presenting four time-dependent queries and deep learning problems that cannot easily be handled in conventional homogeneous graph representations commonly used. As a proof of concept we present a detailed representation of a new social media platform (Steemit), which we use to illustrate both the dynamic querying capability as well as prediction tasks using graph neural networks (GNNs). The results illustrate the power of the dynamic heterogeneous graph representation to model social networks. Given that this is a relatively understudied area we also illustrate opportunities for future work in query optimization as well as new dynamic prediction tasks on heterogeneous graph structures.

We demonstrate Tensor Query Processor (TQP): a query processor that automatically compiles relational operators into tensor programs. By leveraging tensor runtimes such as PyTorch, TQP is able to: (1) integrate with ML tools (e.g., Pandas for data ingestion, Tensorboard for visualization); (2) target different hardware (e.g., CPU, GPU) and software (e.g., browser) backends; and (3) end-to-end accelerate queries containing both relational and ML operators. TQP is generic enough to support the TPC-H benchmark, and it provides performance that is comparable to, and often better than, that of specialized CPU and GPU query processors.

I've made some fundamental modifications to my app which I've distributed via the Archive methodology in Xcode a number of instances earlier than. Nevertheless it isn't working this night as I'm offered with the beneath error: I've ran flutter construct iOS –release with no warnings or errors earlier than making an attempt to Archive in Xcode. I've additionally tried doing a flutter clear too. After I run Validate on the archive earlier than making an attempt to distribute the bundle it comes again with 0 errors. It is simply when I attempt to Distribute the bundle, it comes again with the above error and I am undecided why and even how one can go about diagnosing it. Can anybody please assist level me in the appropriate course?

QuestDB 6.2, our previous minor version release, introduced JIT (Just-in-Time) compiler for SQL filters. As we mentioned last time, the next step would be to parallelize the query execution when suitable to improve the execution time even further and that's what we're going to discuss and benchmark today. QuestDB 6.3 enables JIT compiled filters by default and, what's even more noticeable, includes parallel SQL filter execution optimization allowing us to reduce both cold and hot query execution times quite dramatically. Prior to diving into the implementation details and running some before/after benchmarks for QuestDB, we'll be having a friendly competition with two popular time series and analytical databases, TimescaleDB and ClickHouse. The purpose of the competition is nothing more but an attempt to understand whether our parallel filter execution is worth the hassle or not.