Spatial range queries play a crucial role in spatial information retrieval. Existing Z-order curve based algorithms suffer from accessing a large number of invalid points outside the query region. To address this challenge, we design a simple yet efficient Z-order curve based learned index, ZPI. Building upon ZPI, we propose a novel spatial range query algorithm, ZPI-RQ. ZPI-RQ leverages efficient decomposition mechanism to address the invalid points issue. To avoid decomposing the query region into a large number of overly small blocks, a query region expansion strategy is further introduced to align each query border with a m-order dividing lines. Experimental results show that ZPI-RQ significantly outperforms state-of-the-art algorithms in query efficiency, achieving a 3.6× improvement over traditional Z-order curve-based algorithms while accessing only 1% invalid points.

This paper introduces HQT-TI, a novel indexing method designed to improve the efficiency of spatial keyword queries. HQT-TI consists of two main components: a Hilbert QuadTree (HQT) based spatial index and a Trie-Inverted index (TI) combined textual index. HQT integrates the Hilbert curve with a Quadtree, establishing a direct relationship between the two. TI combines a trie and inverted index to minimize the intersection cost associated with long lists, thus improving the speed of keyword queries. The HQT based Spatial Query algorithm (HQT-SQ) reduces overlap checks and limits irrelevant object retrieval by employing query drill-down and depth first search with limited breadth expansion in spatial queries. Meanwhile, the Segment List Intersection based Keyword Query algorithm (SLI-KQ), built on TI, efficiently handles segment list intersections for keyword queries. The combination of HQT-SQ and SLI-KQ results in HS-SK, a highly efficient spatial keyword query algorithm. Extensive experimental results demonstrate that HS-SKQ outperforms SFC-Quad by up to two orders of magnitude, achieving up to a 5.46× speedup over the best existing competitors, making it a promising solution for large-scale spatial keyword query processing.

Set Similarity Query (SSQ) is widely applied in various fields. The existing B+-tree-based SSQ approaches fail to fully exploit length filtering and require calculating similarity bounds in a node-wise manner, leading to low efficiency. To address these issues, we propose LeB, a novel length-enhanced B+-tree index, whose keys integrate set lengths and bucket mapping, enabling the direct pruning of sets that do not meet the length requirements. Building upon LeB, we present an efficient algorithm, LeBQ, which leverages length filtering and symmetric difference allocation to determine the key bounds for a query, enabling the key bounds computation only once for each query$Q$and avoiding costly similarity bounds computation in a node-wise manner. Efficient key filtering strategies are proposed to prune sets that cannot be similar, significantly reducing the number of candidates. Based on LeBQ, LeBQ+ further reduces the number of candidates by introducing length-independent key bounds. Experimental results on four real datasets demonstrate that LeBQ+ has a higher node access efficiency and accesses only 3.08% to 27.47% nodes compared to the existing B+-tree-based SSQ algorithm. LeBQ+is up to 99.8 × faster than the state-of-the-art algorithms.

The rapid rise in spatial data volumes from diverse sources necessitate efficient spatial data processing capability. Although most relational databases support spatial extensions of SQL query features, they offer limited scalability. Traditional relational database query processing follows a pull-based (or tuple-at-a-time) model of query processing. This is not efficient for processing large volumes of data. A number of specialized spatial data processing systems were developed that extend cluster computing frameworks, such as Spark and Hadoop. However, these systems are characterized by limited or no support for spatial SQL query execution. The few systems that support SQL querying, suffer from the overheads of the pull-based model.We present a compilation-based distributed SQL query processing system. It follows a data-centric query compilation approach that takes a SQL query and generates distributed C++ (UPC++) based physical query plans. The generated code is compiled and executed on a distributed in-memory high performance framework based on the Partitioned Global Address Space (PGAS) paradigm. We also introduce morsel-driven parallelism for scalable spatial query execution in a distributed runtime. We conduct experimental evaluation of our system with two real-world datasets on a number of spatial query workloads. Experimental results demonstrate that our system performs significantly better than a leading spatial big data system Apache Sedona and distributed parallel relational database Citus.

With the technological advances in creation and capture of 3D spatial data, new emerging applications are being developed. Digital Twins, metaverse and extended reality (XR) based immersive environments can be enriched by leveraging geocoded 3D spatial data. Unlike 2D spatial queries, queries involving 3D immersive environments need to take the query user’s viewpoint into account. Spatio-visual queries return objects that are visible from the user’s perspective.In this paper, we propose enhancing 3D spatio-visual queries with large language models (LLM). These kinds of queries allow a user to interact with the visible objects using a natural language interface. We have implemented a proof-of-concept prototype and conducted preliminary evaluation. Our results demonstrate the potential of truly interactive immersive environments.

Driven by the need to support spatial data applications, most relational databases offer spatial SQL query features. However, traditional relational databases are not scalable, and their query processing follows a pull-based tuple-at-a-time model, which is not efficient for large data volumes. Although several specialized spatial data processing systems were developed by extending frameworks, such as Spark and Hadoop, these systems offer limited or no support for spatial SQL query execution. The few systems that support SQL querying, suffer from the overheads of the pull-based model.

Vast amounts of data are stored in raw data files. Data scientists and practitioners typically use data science frameworks for data analysis on raw data. Among them, Python Pandas library is one of the most popular language-based frameworks. On the other hand, relational databases (RDBMSs) are still widely used for SQL query execution. Before querying, raw data must be loaded into RDBMSs through an ETL process. Conversely, data stored in RDBMSs may need to be exported out or moved into a suitable format to perform complex data analysis. This movement of data adversely affects the time-to-insight.

Exploratory data analysis, which is at the heart of data science workflows, is becoming important due to the rapid rise in spatio-temporal data volume, and popularity of Web and mobile mapping applications. Such exploratory data analysis often involves the user selecting an arbitrary polygon region to perform a statistical computation on the selected region. Existing approaches for spatio-temporal data aggregation support rectangular query regions only, and not arbitrary polygons. A recently proposed system called GeoBlocks supports polygonal queries, but GeoBlocks was designed for spatial data, not spatio-temporal data. Another aspect of exploratory data analysis is that the users often repeatedly perform similar statistical analyses over the same selected query region. Although the reuse of already computed answers can improve the response time, existing approaches do not support this reuse for advanced statistical analysis. Data Canopy is a recently proposed approach that supports statistics synthesis by reusing basic aggregates, however, it does not support spatial or spatio-temporal analysis.To address the mentioned challenges, we introduce ScanCube, an exploratory statistical analysis system over any arbitrary polygonal query region for any time interval. ScanCube also supports statistics synthesis by reusing a small set of basic aggregates that are computed and stored a priori. We introduce two techniques, ScanX1 and ScanX2, for providing a grid-based polygonal approximation, which offers distance-based bounded error. Experimental evaluation suggests that ScanCube significantly outperforms GeoBlocks.

In recent years, with the increased usage of the Internet of Things (IoT) devices, cyber-attacks have become a serious threat over the Internet. These devices have low memory capacity and processing power, which makes them easy targets for attackers. The research community has proposed different approaches to deal with emerging variants of attacks on IoT devices using various machine learning techniques. However, these approaches rely heavily on the classifier’s categorization of a given record while ignoring its confidence. This paper proposes a verification-based scheme to reject IoT attacks by utilizing the classifier’s confidence. At the same time, existing studies are evaluated using traditional cross-validation approaches (e.g., k-fold), thus, not tested against unknown attacks. We propose using the leave-one-attack-out (LOAO) cross-validation scheme to evaluate the generalizability of the application to unknown attacks. The experiments are performed on Med BIoT, a publicly available dataset consisting of three IoT attacks. The system’s robustness is evaluated in terms of Receiver Operating Curves (ROC) and Equal Error rates (EERs). The results indicate a lower false-positive rate of 12.6% using the proposed verification-based approach in comparison to k-fold cross-validation.

Due to the rapidly rising data volume, there is a need to analyze this data efficiently and produce results quickly. However, data scientists today need to use different systems, since presently relational databases are primarily used for SQL querying and data science frameworks for complex data analysis. This may incur significant movement of data across multiple systems, which is expensive. Furthermore, with relational databases, the data must be completely loaded into the database before performing any analysis. We believe that data scientists would prefer to use a single system to perform both data analysis tasks and SQL querying, without requiring data movement between different systems. Ideally, this system would offer adequate performance, scalability, built-in data analysis functionalities, and usability. We present DaskDB, a scalable data science system with support for unified data analytics and in situ SQL query processing on heterogeneous data sources. DaskDB supports invoking Python APIs as User-Defined Functions (UDF). So, it can be easily integrated with most existing Python data science applications. Moreover, we introduce a distributed index join algorithm and a novel distributed learned index to improve join performance. Our experimental evaluation involve the TPC-H benchmark and a custom UDF benchmark, which we developed, for data analytics. And, we demonstrate that DaskDB significantly outperforms PySpark and Hive/Hivemall.

Due to the rapid growth in the use of location based services (LBS), abundant spatially referenced text data is being generated. Hence, spatio-textual queries and in particular, spatio-textual join have gained prominence in recent times. Spatio-textual similarity join (STSJ) is an expensive operation, which is used to retrieve documents that are both textually relevant and spatially nearby. NUMA architectures are becoming increasingly prevalent in modern multi-core machines. Applications that are agnostic of the underlying NUMA topology may not be able to fully exploit the hardware. Due to the compute intensive nature of STSJ, efficient processing of STSJ is important, particularly in the context of NUMA architectures. Previous work on spatio-textual similarity join has not addressed this challenge. To remedy this, we explore several approaches to parallelize spatio-textual similarity join on modern NUMA architecture machines. Specifically, we propose three NUMA-aware algorithms. Our best-performing NUMA-aware algorithm exploits topology-aware work-stealing with adaptive data placement. Experimental evaluation involving four real-world datasets (on two different hardware architectures) demonstrates that our NUMA-aware algorithms perform significantly better than existing approaches that do not consider NUMA-awareness.

A corpus of recent work has revealed that the learned index can improve query performance while reducing the storage overhead. It potentially offers an opportunity to address the spatial query processing challenges caused by the surge in location-based services. Although several learned indexes have been proposed to process spatial data, the main idea behind these approaches is to utilize the existing one-dimensional learned models, which requires either converting the spatial data into one-dimensional data or applying the learned model on individual dimensions separately. As a result, these approaches cannot fully utilize or take advantage of the information regarding the spatial distribution of the original spatial data. To this end, in this paper, we exploit it by using the spatial (multi-dimensional) interpolation function as the learned model, which can be directly employed on the spatial data. Specifically, we design an efficient SPatial inteRpolation functIon based Grid index (SPRIG) to process the range and kNN queries. Detailed experiments are conducted on real-world datasets. The results indicate that, compared to the traditional spatial indexes, our proposed learned index can significantly improve the index building and query processing performance with less storage overhead. Moreover, in the best case, our index achieves up to an order of magnitude better performance than ZM-index in range queries and is about 2.7 × , 3 × , and 9 × faster than the multi-dimensional learned index Flood in terms of index building, range queries, and kNN queries, respectively.

With the emergence of wearable technology, IoT, and Edge computing, the nature of healthcare is rapidly shifting towards digital health aided by these ICT technologies. At the same time, consumer devices, such as smart, wearable fitness watches are gaining market share as a way to monitor physical activity and wellness. Despite these advances, and their ability to capture longitudinal behavioural patterns, these devices have yet to be fully leveraged within the healthcare system. If the user-generated data from such devices could be collected without com-promising an individual’s privacy, these insights could comprise part of a more holistic and preventative healthcare solution. In this article, we propose an Edge-assisted data analytics frame-work that uses Federated Learning to re-train local machine learning models using user-generated data. This framework could leverage pre-trained models to extract user-customized insights while preserving privacy and Cloud resources. We also identify some potential application scenarios and discuss research challenges to be explored within the proposed framework.

Spatio-textual similarity join is an operation for finding documents, which are both spatially close and textually relevant. Joins in databases are considered to be the most expensive operation; similarly spatio-textual similarity join is a resource intensive operation. Therefore, it is natural to consider approaches to parallelize this operation.

Data analytics systems commonly utilize in-memory query processing techniques to achieve better throughput and lower latency. Modern computers increasingly rely on Non-Uniform Memory Access (NUMA) architectures to achieve scalability. A key drawback of NUMA architectures is that many existing software solutions are not aware of the underlying NUMA topology and thus do not take full advantage of the hardware. Modern operating systems are designed to provide basic support for NUMA systems. However, default system configurations are typically sub-optimal for large data analytics applications. Additionally, rewriting the application from the ground up is not always feasible.In this work, we evaluate a variety of strategies that aim to accelerate memory-intensive data analytics workloads on NUMA systems. Our findings indicate that the operating system default configurations can be detrimental to query performance. We analyze the impact of different memory allocators, memory placement strategies, thread placement, and kernel-level load balancing and memory management mechanisms. With extensive experimental evaluation, we demonstrate that the methodical application of these techniques can be used to obtain significant speedups in four commonplace in-memory query processing tasks, on three different hardware architectures. Furthermore, we show that these strategies can improve the performance of five popular database systems running a TPC-H workload. Lastly, we summarize our findings in a decision flowchart for practitioners.

The proliferation of location-enabled sensors, smart phones, and the power of digital messaging combined with social media platforms is producing a deluge of multi-dimensional data. Novel index structures are needed to efficiently process massive amounts of geo-tagged data, and to promptly answer queries with textual, spatial, and temporal components. Existing approaches to spatio-temporal text indexing lack a unified index supporting efficient range and top-k search on any combination of location, time, or text.

Location-based services (LBS) are a widely adopted technology that produces large volumes of spatio-temporal data at high velocity. Spatial data is also being generated from many other geo-spatial applications. To address the challenge of data volume, a number of big spatial data management systems have emerged that are based on the MapReduce paradigm. Recent projects have developed spatial data systems using Spark's distributed in-memory architecture. These projects, which include GeoSpark, SpatialSpark, and LocationSpark, do not support the high update rates required by LBS applications. Alternatively, systems such as MD-HBase support data updates, but are hindered by the performance characteristics of HBase, which is a disk-oriented framework. We present DISTIL+, a distributed spatio-temporal data processing system designed for high velocity location data. Our system achieves high update throughput and low query latency by leveraging the APGAS (Asynchronous Partitioned Global Address Space) architecture to build a multi-level distributed in-memory index. We present extensive experimental evaluation of our system, comparing several indexing and data placement schemes, as well as competing systems. Our results show that DISTIL+ excels at supporting high throughput location updates, and low latency spatio-temporal range queries and kNN queries, while offering better performance than existing approaches.

Location-based services (LBS) have become an ubiquitous technology and spatio-temporal data generated by LBS is characterized by high volume and velocity. In recent times several projects, such as GeoSpark, SpatialSpark and LocationSpark, have focused on developing spatial data systems that take advantage of the distributed in-memory data processing capability of Spark. However, most of these systems assume immutable spatial data, and they do not support high throughput location data updates that are common in LBS. On the other hand, a few HBase-based systems, such as MD-HBase, have been proposed that support data updates. However, these systems do not take advantage of any distributed in-memory query processing frameworks.

Main memory hash joins are an important category of in-memory joins. However, the performance of these joins can be hindered by dataset skew, shuffling, and load balancing. We conducted a comprehensive study on the effects of dataset skew on four hash join algorithms. We show that hash joins are acutely affected by dataset skew, and the performance gets worse with shuffled data. To address these issues, we propose non-partitioning hash joins using two different hash tables. First, we use a separate chaining hash table that is based on an existing implementation that we have modified. This version outperforms the original implementation on skewed datasets by up to three orders of magnitude. Second, we propose a novel hash table for hash joins, called Maple hash table. We demonstrate that this hash table is better suited to skewed and/or shuffled datasets. Moreover, this approach further improves performance by up to 17.3×.

With the growing number of Open Data initiatives and the increased volume of related data, new forms of civic engagement are emerging. This engagement leads to novel applications and problem solving approaches. Useful insights derived from exploiting publicly available data and open source tools ultimately result in the enhancement of daily life in our communities. In this paper, we analyze publicly available road accident data for the cities of Fredericton and Laval. A traffic accident data processing and analysis pipeline is built using big data systems and big data spatial frameworks. We present a comparative analysis of traffic accidents in these two cities. Random forest classification models are trained to predict if an accident has casualties. The predictive models are used to provide insights into important factors affecting accidents with fatalities or injuries. Results of the study can be used to establish meaningful safe driving policy suggestions, aid in making emergency dispatch decisions, inform accident management procedures or even assist with urban planning.

The volume of data that is generated each day is rising rapidly. There is a need to analyze this data efficiently and produce results quickly. Data science offers a formal methodology for processing and analyzing data. It involves a work-flow with multiple stages, such as, data collection, data wrangling, statistical analysis and machine learning. In this paper, we look at data analytics systems that support the data science work-flow. The variety of current commercial and open-source data analytics systems differ significantly in terms of available features, functionality, and scalability. A benchmark can be used to evaluate the functionality and performance of a system. However, there is no standard benchmark for evaluating or comparing these data systems for doing data science. In this paper, we introduce a data science benchmark, Sanzu, to evaluate systems with data processing and analytics tasks. Our benchmark includes a micro and macro benchmark. The micro benchmark tests basic operations in isolation. It consists of task suites for reading and writing, data wrangling, statistical analysis, machine learning and time series analysis. Each macro workload evaluates an analytics application where a series of analysis or functions are based on a real world application. The macro benchmark focuses on sports and smart grid analytics. We evaluate these tasks on five different popular data science frameworks and systems: R, Anaconda Python, Dask, PostgreSQL (MADlib) and PySpark. For micro benchmark we generate synthetic datasets with 3 scale factors: 1, 10 and 100 (scale factor 1=1 million). The macro benchmark uses data generated from real-world data sources.

Every year research institutions produce petabytes of data. Yet, only a small percent of the data is readily accessible for analysis. Terra Populus acts as the bridge between big data sources and researchers. Researchers are provided convenient web applications that allow them to access, analyze, and tabulate different datasets under a common platform. Terra Populus is developing three unique applications. The first application, Paragon, is a prototype parallel spatial database, which aims to extend the functionality of PostgreSQL and PostGIS onto multinode systems. Terra Populus' Tabulator application employs Parquet on Spark to build dynamic queries for analyzing large population survey data. The last application, Terra Explorer, is an exploratory analysis tool for visualizing the spatial datasets within the repository.

The rapid growth of spatiotemporal Big Data is fueling the emergence and growth of many applications. Many of these applications are characterized by complex spatiotemporal queries. An important category of such queries is the trajectory-based spatiotemporal topological join queries, which combine a trajectory dataset and a spatial objects dataset based on spatiotemporal predicates. Although these queries have many important use-cases, they have not received much attention from the research community. We systematically evaluate several feasible in-memory spatiotemporal topological join algorithms, using existing trajectory index (TB-tree) and spatial index (STR). We show that even the best among these algorithms is long running and not scalable. To address the performance problems of these algorithms we introduce PISTON, a parallel in-memory indexing system targeted for spatiotemporal topological join. With extensive evaluations, we demonstrate that even the single-threaded performance of PISTON is significantly better than the feasible approaches that use existing trajectory and spatial indexes. Moreover, the parallel performance of PISTON is orders of magnitude better than these approaches.

Traditional relational database engines have been losing ground to specialized data processing engines in virtually every market segment, from data warehousing, OLTP, and stream processing, to scientific applications. Although relational database engines are evolving to leverage new technologies and more efficient processing paradigms, the generality of a large monolithic engine often makes this a significant effort. Our aim is to delimit and decouple database engine components to design a more lightweight and flexible data processing engine that can support any application domain efficiently and without the effort of a complete redesign. We introduce Slingshot, a new data processing engine, where modularity and implementation flexibility are the top priority. Its core database engine is minimal and mainly handles inter-operation of the database components. Each component, abstracted by an interface, can be externally implemented and plugged into the framework as a module that handles the component's functionality. As a result, this allows designers the liberty to choose suitable features for their target applications, to drop excess functionality, and to optimize code independent of the rest of the engine. We compare Slingshot to a traditional RDBMS and to custom solutions on queries that are representative of three application types (spatial, OLAP, and OLTP). We show that Slingshot outperforms the RDBMS in most cases, while performing comparably in others. Furthermore, Slingshot performs better or comparable to custom solutions on most tests. Finally, Slingshot's flexibility allows us to efficiently leverage computer architectures such as GPUs for speeding up complex computational tasks.

The volume of spatio-temporal data is growing at a rapid pace. This is driven by several factors, including the widespread adoption of GPS-enabled mobile devices and the proliferation of RFID-tagged objects in sensor networks. Besides the volume, such spatio-temporal data is characterized by high "velocity", with its high rate of time-stamped location updates. The rise of spatio-temporal "Big data" has led to the emergence of many novel location-oriented applications. These applications often have complex use-cases and service-level requirements. Efficient management of the spatio-temporal data is critical to meet these requirements. This poses some challenges and unique research questions, for instance: i) how to support the high rate of location updates, while at the same time supporting many concurrent historical, present and predictive queries, ii) what kind of database storage organization is suitable for such workload, iii) what are the implications for the spatio-temporal index, and iv) what kind of novel spatio-temporal queries are to be supported. Technological trends involving increasingly large main memory sizes and core counts offer opportunities to address some of these issues. We have addressed a few issues pertinent to high performance commercial Location-Based Services (LBS) by exploiting in-memory database techniques. We propose an in-memory storage organization for high insert performance and introduce a novel spatio-temporal index. With extensive evaluation, we demonstrate that our system supports high insert and query throughputs and it outperforms the leading LBS system by a significant margin. As part our future research we are building a spatio-temporal data management system in the context of a cluster of machines in the Cloud. We are also investigating the possibility of supporting trajectory-based join queries.

With the proliferation of mobile devices and explosive growth of spatio-temporal data, Location-Based Services (LBS) have become an indispensable technology in our daily lives. The key characteristics of the LBS applications include a high rate of time-stamped location updates, and many concurrent historical, present and predictive queries. The commercial providers of LBS must support all three kinds of queries and address the high update rates. While they employ relational databases for this purpose, traditional databases are unable to cope with the growing demands of many LBS systems. Support for spatio-temporal indexes within these databases are limited to R-tree based approaches. Although a number of advanced spatio-temporal indexes have been proposed by the research community, only a few of them support historical queries. These indexing techniques, with support for historical queries, are unable to sustain high update and query throughput typical in LBS. Technological trends involving increasingly large main memory and core footprints offer opportunities to address some of these issues. We present several key ideas to support high performance commercial LBS by exploiting in-memory database techniques. Taking advantage of very large memory available in modern machines, our system maintains the location data and index for the past N days in memory. Older data and index are kept in disk. We propose an in-memory storage organization for high insert performance. We also introduce a novel spatio-temporal index that maintains partial temporal indexes in a versioned-grid structure. The partial temporal indexes are organized as compressed bitmaps. With extensive evaluation, we demonstrate that our system supports high insert and query throughputs and it outperforms the leading LBS system by a significant margin.

Spatial join is a crucial operation in many spatial analysis applications in scientific and geographical information systems. Due to the compute-intensive nature of spatial predicate evaluation, spatial join queries can be slow even with a moderate sized dataset. Efficient parallelization of spatial join is therefore essential to achieve acceptable performance for many spatial applications. Technological trends, including the rising core count and increasingly large main memory, hold great promise in this regard. Previous parallel spatial join approaches tried to partition the dataset so that the number of spatial objects in each partition was as equal as possible. They also focused only on the filter step. However, when the more compute-intensive refinement step is included, significant processing skew may arise due to the uneven size of the objects. This processing skew significantly limits the achievable parallel performance of the spatial join queries, as the longest-running spatial partition determines the overall query execution time.

Spatial data analysis applications are emerging from a wide range of domains such as building information management, environmental assessments and medical imaging. Time-consuming computational geometry algorithms make these applications slow, even for medium-sized datasets. At the same time, there is a rapid expansion in available processing cores, through multicore machines and Cloud computing. The confluence of these trends demands effective parallelization of spatial query processing. Unfortunately, traditional parallel spatial databases are ill-equipped to deal with the performance heterogeneity that is common in the Cloud.

Spatial databases are increasingly important for a wide variety of real-world applications, such as land surveying, urban planning, cartography and location-based services. However, spatial database workload properties are not well-understood. For example, it is unknown to what degree one spatial application resembles another in terms of resource demand, or how the demand will change as more concurrent queries (i.e., more users) are added. We show that spatial workloads have a different CPU execution profile than well-studied decision support workloads, as represented by TPC-H.

The volume of spatial data generated and consumed is rising exponentially and new applications are emerging as the costs of storage, processing power and network bandwidth continue to decline. Database support for spatial operations is fast becoming a necessity rather than a niche feature provided by a few products. However, the spatial functionality offered by current commercial and open-source relational databases differs significantly in terms of available features, true geodetic support, spatial functions and indexing. Benchmarks play a crucial role in evaluating the functionality and performance of a particular database, both for application users and developers, and for the database developers themselves. In contrast to transaction processing, however, there is no standard, widely used benchmark for spatial database operations. In this paper, we present a spatial database benchmark called Jackpine. Our benchmark is portable (it can support any database with a JDBC driver implementation) and includes both micro benchmarks and macro workload scenarios. The micro benchmark component tests basic spatial operations in isolation; it consists of queries based on the Dimensionally Extended 9-intersection model of topological relations and queries based on spatial analysis functions. Each macro workload includes a series of queries that are based on a common spatial data application. These macro scenarios include map search and browsing, geocoding, reverse geocoding, flood risk analysis, land information management and toxic spill analysis. We use Jackpine to evaluate the spatial features in 2 open source databases and 1 commercial offering.