This dataset contains results of various metric tests performed in the SPARQL query engine nLDE: the network of Linked Data Eddies, in different configurations. The queries themselves are available via the nLDE website and tests are explained in depth within the associated publication.To compute the diefficiency metrics dief@t and dief@k, we need the answer trace produced by the SPARQL query engines when executing queries. Answer traces record the exact point in time when an engine produces an answer when executing a query.We executed SPARQL queries using three different configurations of the nLDE engine: Selective, NotAdaptive, Random. The resulting answer trace for each query execution is stored in the CSV file nLDEBenchmark1AnswerTrace.csv. The structure of this file is as follows: query: id of the query executed. Example: 'Q9.sparql'approach: name of the approach (or engine) used to execute the query.tuple: the value i indicates that this row corresponds to the ith answer produced by approach when executing query.time: elapsed time (in seconds) since approach started the execution of query until the answer i is produced.In addition, to compare the performance of the nLDE engine using the metrics dief@t and dief@k as well as conventional metrics used in the query processing literature, such as: execution time, time for the first tuple, and number of answers produced. We measured the performance of the nLDE engine using conventional metrics. The results are available at the CSV file inLDEBenchmark1Metrics. The structure of this CSV file is as follows:query: id of the query executed. Example: 'Q9.sparql'approach: name of the approach (or engine) used to execute the query.tfft: time (in seconds) required by approach to produce the first tuple when executing query.totaltime: elapsed time (in seconds) since approach started the execution of query until the last answer of query is produced.comp: number of answers produced by approach when executing query.

The HTSQL Interface extension for CKAN enhances data access by enabling users to query the CKAN datastore using the powerful HTSQL query language. This provides an alternative to the standard CKAN API, offering more flexibility and expressiveness in data retrieval. By adding a new API endpoint, datastoresearchhtsql, the extension allows for complex data manipulations and selections directly within the CKAN environment. Key Features: HTSQL Query Endpoint: Introduces a dedicated API endpoint (datastoresearchhtsql) to execute HTSQL queries against the datastore. Enhanced Data Retrieval: Enables users to perform more sophisticated data filtering, aggregation, and transformation compared to the standard CKAN datastore search API. Datastore Integration: Leverages CKAN's datastore functionality, allowing HTSQL queries on data resources stored within CKAN. Installation Simplicity: Installs as a standard CKAN extension through pip and is activated via the CKAN configuration file. Technical Integration: The HTSQL extension integrates tightly with the CKAN datastore by adding the datastoresearchhtsql API endpoint. To implement, one would install it via pip, followed by adding htsql to the ckan.plugins line in the CKAN .ini configuration file. This activates the extension and makes the HTSQL query functionality available. Benefits & Impact: The HTSQL Interface extension provides CKAN users with a more powerful and versatile way to query data stored in the datastore. This enhanced query capability can lead to more efficient data analysis, reporting, and application development by easing the complexity of data requests. By providing an alternative to the standard API, the extension offers greater control and flexibility in extracting valuable insights from data resources.

Physical Properties of Rivers: Querying Metadata and Discharge Data

This lesson was adapted from educational material written by Dr. Kateri Salk for her Fall 2019 Hydrologic Data Analysis course at Duke University. This is the second part of a two-part exercise focusing on the physical properties of rivers.

Introduction

Rivers are bodies of freshwater flowing from higher elevations to lower elevations due to the force of gravity. One of the most important physical characteristics of a stream or river is discharge, the volume of water moving through the river or stream over a given amount of time. Discharge can be measured directly by measuring the velocity of flow in several spots in a stream and multiplying the flow velocity over the cross-sectional area of the stream. However, this method is effort-intensive. This exercise will demonstrate how to approximate discharge by developing a rating curve for a stream at a given sampling point. You will also learn to query metadata from and compare discharge patterns in climatically different regions of the United States.

Learning Objectives

After successfully completing this exercise, you will be able to:

1. Execute queries to pull a variety of National Water Information System (NWIS) and Water Quality Portal (WQP) data into R.
2. Analyze seasonal and interannual characteristics of stream discharge and compare discharge patterns in different regions of the United States

ckanext-sql Due to the absence of a README file in the provided GitHub repository for ckanext-sql, a comprehensive understanding of its features, integration, and benefits is unfortunately not available. Typically, an extension named 'sql' would likely bridge CKAN with SQL databases, potentially enabling users to query and interact with datasets stored in SQL-compatible databases directly from within CKAN. However, lacking specific documentation, definitive claims about its capabilities cannot be accurately made. Potential Key Features (based on the name and typical use cases): * SQL Query Interface: Hypothetically, this extension might offer an interface within CKAN to run SQL queries against linked datasets. * Data Visualization from SQL: Potentially, it could allow generating visualizations directly from data retrieved via SQL queries. * SQL Data Import: It is possible that the extension could provide functionality to import data from SQL databases into CKAN datasets. * Federated Queries: Maybe, the extension implements capability of running federated queries across datasets store as CKAN resources and external databases. * SQL Data Export: Possibility of offering the ability to export CKAN data to a SQL database. * SQL based resource views: Speculatively add different views for resource showing data from SQL Potential Use Cases (based on the name): 1. Direct Data Analysis: Data analysts might use this to directly query and analyze data stored in SQL databases via CKAN, skipping manually importing the data. 2. Database Integration: Organizations that already have large databases of data could use this extension to provide easier access to this data through a CKAN portal. Technical Integration (Hypothetical): Given the name, the 'sql' extension likely integrates with CKAN by adding new API endpoints or UI elements that allow users to specify SQL connections and queries. It would probably require configuration settings to define database connection parameters. It might also integrate with CKAN's resource view system, enabling custom visualizations. Potential Benefits & Impact (Speculative): If the extension functions as expected by the name, it would offer direct access to SQL data within the CKAN environment, reduce the need for data duplication (by querying directly rather than importing), and potentially enhance data analysis and visualization capabilities. The extension could become an indispensable part of data analytic workflows involving CKAN. However, due to a lack of a README.md, this analysis remains at theoretical level.

Benchmark Execution with- 250 Feasible Queries- 400M Triple DBpedia Dataset- 16 querying user- 16 update user- 250 changesets

This dataset is composed of the 28 SPARQL queries executed to generate the measurement tables which are included in the files belonging to dataset containing the data tables results of the queries execution. They have the same name. They only differ by their extension. By example, CWG_reception_fallingNumber_raw.sparql is the file including the SPARQL query executed to obtain the table included in the file CWG_reception_fallingNumber_raw.tsv.

This dataset provides an annual count of Pierce County's tribal consultation memorandums.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Collection of raw results, figures and gnumeric file for the figures of the execution of the DBPSBv2 queries with IGUANA on an 400M Triple Dbpedia dataset.- results_again.zip : raw results- results_dbpsb.gnumeric : gnumeric file to create the figures- dbpsb_16_qmph.pdf : Figure for 16 querying users and a comparison between 0 and 1 update user (Metric: Query Mixes Per Hour)- dbpsb_16_no-of-queries.pdf : Figure for 16 querying users and a comparison between 0 and 1 update user (Metric: No of queries Per Hour)- dbpsb_16-1_qps.pdf : Figure for 16 querying users and 1 update user (Metric: Query Per Second)- dbpsb_16-0_qps.pdf : Figure for 16 querying users and no update user (Metric: Query Per Second)

The graphql extension for CKAN introduces a GraphQL API endpoint, providing an alternative method to query CKAN data in addition to the existing Action API. Designed for CKAN instances running version 2.7 or later, this extension allows users to retrieve information about datasets, groups, and organizations using GraphQL queries. While still under development, it aims to offer a flexible way to access and manipulate CKAN data. Key Features: GraphQL Endpoint: Provides a /graphql endpoint on the CKAN instance to execute GraphQL queries. GraphiQL Integration: Includes GraphiQL, an in-browser IDE, for composing and testing GraphQL queries directly within the CKAN interface. Package Querying: Allows querying of packages, including related groups and organizations. Search Functionality: Supports searching for packages and groups based on specific terms. Extensible Schema: Intends to offer an interface to extend or customize the GraphQL schema from other CKAN extensions (e.g., to add custom models). Support for Mutations (Future): Plans to include the ability to modify data using GraphQL mutations. Technical Integration: The extension integrates with CKAN by adding a new plugin that exposes the GraphQL endpoint. Enabling the graphql plugin in the CKAN configuration file (.ini) makes the endpoint available. It intends to have schema customization options so other extensions can add their own models which would be done in a configuration file. Benefits & Impact: Utilizing the graphql extension can simplify data retrieval from CKAN by providing a standardized and queryable GraphQL interface. This allows users to request specific data they need, thereby reducing the amount of data transfer and improving performance. The extension aims to become a viable alternative to the CKAN Action API for data querying.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Survey results in key IT areas

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Validation queries for the paper OKG-Soft: An Open Knowledge Graph with Machine Readable Scientific Software Metadata. The queries are shown in SPARQL along with the results obtained when they were executed. Note that since the execution of these queries the vocabularies and knowledge graph may have slightly changed and small changes may be needed to execute the query.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Performance measures dataset

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This dataset feeds the view that powers the Open Budget app.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Performance measures dataset

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This data set contains the questions associated with each SeeClickFix request. Each line is a question within a request. A single request may have multiple lines due to the questions asked within the request.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This dataset is a complete inventory of all assets on this site and any assets sourced from other sites, if applicable.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.