In modern organizations, data management is a major issue and at the same time a major resource. In our experience, the first challenge a business that wants to use its data is facing how to have a unified view of their data. Generally data inside organizations is stored in different databases that have often proprietary API making it difficult to move from one database to the other. Furthermore, also when the technology used to store data is the same, there are still semantic problems like different terminologies, languages etc.
The bigger the company is, the lower the possibility to standardize the procedures are, so that these kind of situations will not happen. This happens because we are human and we naturally tend to interpret data using our own experience and knowledge. Thus we cannot expect the technical team to call all pieces of a car using the exact same terminology as the logistic department. This is why, our solution aims at giving the possibility to standardize the way in which the end user interact with the data without actually changing the source of the data.
Ask Data Anything (ADA), allows companies to add a semantical layer on top of the data without the need of copying data. The product is managing term disambiguation, aggregation of data using hierarchies defined in ontologies, data integration between different data sources.
This article presents usage of ADA tool with sample data about election results in Leeds borough in 2014, coming from DATA.GOV.UK (http://data.gov.uk/dataset/election-results). It presents example operation which can be performed over data: firstly - summarizing results, then aggregation and finally projection. All sections include example queries and results view. Overall knowledge, consisting of information form data file (which can be e.g. CSV file) and taxonomy in form of CNL ontology, allows ADA to give answers for statistical queries like parties' results in different regions as well as results for specific candidates. ADA is capable of handling queries involving time, location and both predefined and user's concepts.
Example query:
Summarize party by result on piechart
Example query:
Sum votes by party on histogram
Example query:
Sum votes by party in Kirkstall
It is possible to combine restrictions to get answers to more sophisticated queries. Once again one can ask for votes given to parties, but this time the expected outcome is the average result in two boroughs: Kirkstall and Beeston.
Example query:
Sum votes by party in Kirkstall and Beeston on histogram
Example query:
Surname in Alwoodley with votes > 1000
Besides strictly statistical data, one can also ask for more specific information. Example presented in picture below shows all parties which candidates received mandates in Otley borough.
Example query:
Party in Otley with mandate > 0
Ask Data Anything supports data exploration by applying semantic layer on top of the raw data, which allows to execute analysis without explicitly stating all information. It enables to perform variety of operations, like aggregation or projection, over data set when needed. ADA allows to formulate queries in intuitive way using natural language and present results in convenient for users form (on table, histogram, map or pie chart). Expanding taxonomy makes it possible to ask more complex questions and extends knowledge base with minimal effort.
The bigger the company is, the lower the possibility to standardize the procedures are, so that these kind of situations will not happen. This happens because we are human and we naturally tend to interpret data using our own experience and knowledge. Thus we cannot expect the technical team to call all pieces of a car using the exact same terminology as the logistic department. This is why, our solution aims at giving the possibility to standardize the way in which the end user interact with the data without actually changing the source of the data.
Ask Data Anything (ADA), allows companies to add a semantical layer on top of the data without the need of copying data. The product is managing term disambiguation, aggregation of data using hierarchies defined in ontologies, data integration between different data sources.
This article presents usage of ADA tool with sample data about election results in Leeds borough in 2014, coming from DATA.GOV.UK (http://data.gov.uk/dataset/election-results). It presents example operation which can be performed over data: firstly - summarizing results, then aggregation and finally projection. All sections include example queries and results view. Overall knowledge, consisting of information form data file (which can be e.g. CSV file) and taxonomy in form of CNL ontology, allows ADA to give answers for statistical queries like parties' results in different regions as well as results for specific candidates. ADA is capable of handling queries involving time, location and both predefined and user's concepts.
Summarizing data
One of the possibilities is to ask ADA to summarize parties' general results and present them on pie chart. Query result's computation is based on data contained in data file and semantically modelled taxonomy. Thanks to that knowledge ADA's engine can recognize concepts included in query and perform aggregation on demand.Example query:
Summarize party by result on piechart
Performing aggregation
There are many possible types of output for each result: table, histogram, pie chart and map. It is also possible to perform different types of aggregation on numerical values, like sum or average. Picture below presents overall sum of votes for each party in form of histogram:Example query:
Sum votes by party on histogram
Limiting the aggregation area
Domain of operation can be limited to specified area, declared in taxonomy. Here we present the result for a question similar to previous one, but where votes from Kirkstall borough only are used. This time output type was set to the table, which is default option.Example query:
Sum votes by party in Kirkstall
It is possible to combine restrictions to get answers to more sophisticated queries. Once again one can ask for votes given to parties, but this time the expected outcome is the average result in two boroughs: Kirkstall and Beeston.
Example query:
Sum votes by party in Kirkstall and Beeston on histogram
Performing projection over data
Another way to manipulate queries' meaning is by using "with" keyword. Clause added after "with" allows to filter output to contain only specific results, as in query presented below:Example query:
Surname in Alwoodley with votes > 1000
Besides strictly statistical data, one can also ask for more specific information. Example presented in picture below shows all parties which candidates received mandates in Otley borough.
Example query:
Party in Otley with mandate > 0
Summary
Ask Data Anything supports data exploration by applying semantic layer on top of the raw data, which allows to execute analysis without explicitly stating all information. It enables to perform variety of operations, like aggregation or projection, over data set when needed. ADA allows to formulate queries in intuitive way using natural language and present results in convenient for users form (on table, histogram, map or pie chart). Expanding taxonomy makes it possible to ask more complex questions and extends knowledge base with minimal effort.
References
- Semantic OLAP with FluentEditor and Ontorion Semantic Excel Toolchain
- Ask Data Anything - Quick Overview Video
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ReplyDeleteThe "Ask Data Anything - Election Results Example" tool is a versatile and powerful tool for data exploration. Its user-friendly interface allows users to input queries and receive instant, relevant data. Real-time data updates are impressive, allowing users to access the latest election results effortlessly. The ability to filter and refine results based on various criteria provides a comprehensive understanding of election outcomes. Visual options are engaging and aid in quickly grasping key trends and patterns in the data. The tool's responsiveness and speed in generating results make it highly efficient for users seeking immediate information. The natural language query feature simplifies the process, making data analysis accessible to all. The tool incorporates historical election data alongside current results, providing valuable context for users. The interactive map feature allows users to explore election outcomes at a regional level. The accuracy and reliability of the data are essential for making informed analyses. The 'compare elections' feature allows users to juxtapose different election years for meaningful insights. The option to export data and visualizations for further analysis is valuable for researchers and analysts. The tool fosters data-driven discussions and encourages users to delve deeper into election dynamics. The responsive design ensures that the tool is user-friendly across various devices, and the data sources and citations are transparent. The 'voter turnout' feature provides valuable insights into civic engagement and participation trends. The user-friendly layout and intuitive navigation contribute to a seamless user experience.
Naturally, of course! I'd be pleased to offer the following example of a question to pose regarding election results:
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Assistant: "That's right! The following are the election results for the US presidential race in 2020:
Democratic Party's Joe Biden received 306 electoral votes.
232 electoral votes went to Republican candidate Donald Trump.
After winning the election, Joe Biden was sworn in as the 46th President of the United States. Do you have any further questions concerning elections, or is there anything special you would like to know about these election results?"
Wow, what an insightful blog post! The example of election results really showcases the power of data. It's fascinating how data can help us analyze and understand the intricacies of elections. I have always been intrigued by the role data plays in shaping our political landscape.
ReplyDeleteI have a couple of questions after reading this article. Firstly, I'm curious about the sources of data used to analyze the election results. Were they official sources or were there other datasets involved? It would be helpful to understand the reliability and accuracy of the data used.
Secondly, what tools or techniques were employed to visualize and present the election data? The visual representations mentioned in the article sounded intriguing, and I would love to know more about the methodology behind them.
Overall, I found this article both informative and thought-provoking. It has definitely heightened my interest in data analysis and how it can be applied to real-world scenarios like elections. Keep up the great work!
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This is great post and very informative article. Generally data inside organizations is stored in different databases that have often proprietary API making it difficult to move from one database to the other. Furthermore, also when the technology used to store data is the same, there are still semantic problems like different terminologies, languages etc.
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This article discusses Ask Data Anything (ADA), a tool that provides a semantic layer over raw data, allowing users to interact with it intuitively using natural language queries. ADA facilitates operations like summarizing, aggregating, and projecting data, allowing users to interact with it without altering the original datasets. The tool uses a sample dataset of election results from Leeds borough in 2014, sourced from DATA.GOV.UK. It generates visual representations like pie charts, histograms, and tables based on user queries, such as summarizing party results, aggregating votes by party or region, and projecting specific data based on specific criteria. ADA operates by understanding the underlying concepts within queries and manipulating the data accordingly. It emphasizes the importance of a standardized approach to data interpretation while acknowledging the challenges posed by different databases, terminologies, and human interpretation. ADA's strength lies in its ability to understand and interpret user queries, handle semantic disambiguation, integrate data from different sources, and apply predefined ontologies for better comprehension of the data structure. Its intuitive and user-friendly interface for data exploration and analysis is poised to streamline the process of deriving insights from complex datasets, catering to users across different departments within an organization. Lawyer Estate
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