The importance of geo-spatial data in critical applications such as emergency response, transportation, agriculture etc., has prompted the adoption of recent GeoSPARQL standard in many RDF processing engines. In addition to large repositories of geo-spatial data -- e.g., LinkedGeoData, OpenStreetMap, etc. -- spatial data is also routinely found in automatically constructed knowledgebases such as Yago and WikiData. While there have been research efforts for efficient processing of spatial data in RDF SPARQL, very little effort has gone into building end-to-end systems that can holistically handle complex SPARQL queries along with spatial filters. In this paper, we present Streak, a RDF data management system that is designed to support a wide-range of queries with spatial filters including complex joins, top-k, higher-order relationships over spatially enriched databases. Streak introduces various novel features such as a careful identifier encoding strategy for spatial and non-spatial entities, the use of a semantics-aware Quad-tree index that allows for early-termination and a clever use of adaptive query processing with zero plan-switch cost. We show that Streak can scale to some of the largest publicly available semantic data resources such as Yago3 and LinkedGeoData which contain spatial entities and quantifiable predicates useful for result ranking. For experimental evaluations, we focus on top-k distance join queries and demonstrate that Streak outperforms popular spatial join algorithms as well as state of the art end-to-end systems like Virtuoso and PostgreSQL.
Eddies @cite_5 and Content Based Retrieval (CBR) @cite_18 are two state-of-the-art approaches which explored AQP for switching plans during query execution. Both these approaches use random tuples for profiling, relying on a machine-learning based solution to model routing predictions. Our work directly contrasts ML-based approaches by using statistics for each of spatial data, which helps in determining the selectivity of spatial join operator. By using fine-grained block-level statistics, we mitigate the errors that are typically associated with a model-based approach, especially when there are many joins involved. Additionally, unlike CBR, which has high routing and learning overhead, our spatial AQP algorithm incurs a very small routing overhead, owing to cost estimate calculations for only the customized plans. We validated this argument and found the overhead of AQP to be very small --- just 5-10$
@cite_5 @cite_18
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Query optimizers in current database systems are designed to pick a single efficient plan for a given query based on current statistical properties of the data. However, different subsets of the data can sometimes have very different statistical properties. In such scenarios it can be more efficient to process different subsets of the data for a query using different plans. We propose a new query processing technique called content-based routing (CBR) that eliminates the single-plan restriction in current systems. We present low-overhead adaptive algorithms that partition input data based on statistical properties relevant to query execution strategies, and efficiently route individual tuples through customized plans based on their partition. We have implemented CBR as an extension to the Eddies query processor in the TelegraphCQ system, and we present an extensive experimental evaluation showing the significant performance benefits of CBR. A modern query optimizer typically picks a single query plan for all data based on overall data statistics. However, many have observed that real-life datasets tend to have non-uniform distributions. Selecting a single query plan may result in ineffective query execution for possibly large portions of the actual data. In addition most stream query processing systems, given the volume of data, cannot precisely model the system state much less account for uncertainty due to continuous variations. Such systems select a single query plan based upon imprecise statistics. In this paper, we present ''Query Mesh'' (or QM), a practical alternative to state-of-the-art data stream processing approaches. The main idea of QM is to compute multiple routes (i.e., query plans), each designed for a particular subset of the data with distinct statistical properties. We use terms ''plans'' and ''routes'' interchangeably in our work. A classifier model is induced and used to assign the best route to process incoming tuples based upon their data characteristics. We formulate the QM search space and analyze its complexity. Due to the substantial search space, we propose several cost-based query optimization heuristics designed to effectively find nearly optimal QMs. We propose the Self-Routing Fabric (SRF) infrastructure that supports query execution with multiple plans without physically constructing their topologies nor using a central router like Eddy. We also consider how to support uncertain route specification and execution in QM which can occur when imprecise statistics lead to more than one optimal route for a subset of data. Our experimental results indicate that QM consistently provides better query execution performance and incurs negligible overhead compared to the alternative state-of-the-art data stream approaches. In large federated and shared-nothing databases, resources can exhibit widely fluctuating characteristics. Assumptions made at the time a query is submitted will rarely hold throughout the duration of query processing. As a result, traditional static query optimization and execution techniques are ineffective in these environments. In this paper we introduce a query processing mechanism called an eddy, which continuously reorders operators in a query plan as it runs. We characterize the moments of symmetry during which pipelined joins can be easily reordered, and the synchronization barriers that require inputs from different sources to be coordinated. By combining eddies with appropriate join algorithms, we merge the optimization and execution phases of query processing, allowing each tuple to have a flexible ordering of the query operators. This flexibility is controlled by a combination of fluid dynamics and a simple learning algorithm. Our initial implementation demonstrates promising results, with eddies performing nearly as well as a static optimizer executor in static scenarios, and providing dramatic improvements in dynamic execution environments. This paper focuses on the problem of Question Routing (QR) in Community Question Answering (CQA), which aims to route newly posted questions to the potential answerers who are most likely to answer them. Traditional methods to solve this problem only consider the text similarity features between the newly posted question and the user profile, while ignoring the important statistical features, including the question-specific statistical feature and the user-specific statistical features. Moreover, traditional methods are based on unsupervised learning, which is not easy to introduce the rich features into them. This paper proposes a general framework based on the learning to rank concepts for QR. Training sets consist of triples (q, asker, answerers) are first collected. Then, by introducing the intrinsic relationships between the asker and the answerers in each CQA session to capture the intrinsic labels orders of the users about their expertise degree of the question q, two different methods, including the SVM-based and RankingSVM-based methods, are presented to learn the models with different example creation processes from the training set. Finally, the potential answerers are ranked using the trained models. Extensive experiments conducted on a real world CQA dataset from Stack Overflow show that our proposed two methods can both outperform the traditional query likelihood language model (QLLM) as well as the state-of-the-art Latent Dirichlet Allocation based model (LDA). Specifically, the RankingSVM-based method achieves statistical significant improvements over the SVM-based method and has gained the best performance.