Is there a page where one can se statistics about the current planet dataset, for example: what is the number of points in the largest geometry (or maybe a distribution of the number of points)?
Any statistics page would be useful.
Here are various statistics, especially the database statistics and taginfo might be of interest for you. Also don't miss the list of external statistics.
Related
I need to plot trend charts on the react app based on user inputs such as timestamps, devices, etc. I have related time series data in DynamoDB and S3 (which I can query using Athena).
Returning all those millions of data points for a graph seems unreasonable and is super laggy.
I guess one option is "binning" where I decide the number of bins based on how big the time range is and take averages of the readings in that bin. However, concerned about how well it will show the drops and high we need to show them accurately.
Athena queries and DDB queries (due to the 1MB limit) - both seem fairly slow so far.
Of course the size of the response payload is another concern as API and Lambda both limit it to 10 and 6Mb respectively.
Any ideas?
I can't suggest anything smarter than "binning", but if you are concerned that the bucket interval might become too wide and performance might suffer, you can fixate the interval. Then create more than one table. For example, the interval can be 1 hour and you can have a new table for each week.
This is what we did when we had to deal with time series in dynamo. At some point, we decided to switch to Amazon Timestream
How can I get speed limit for multilevel intersections/roads? When I go over the bridge or under the bridge, I can get wrong speed limit.
I am using: way[maxspeed](around:20, <latitude>, <longitude>), but I cannot specific altitude.
I am using Overpass API by OpenStreetMaps.
Unfortunately, your current approach of considering speed limits of any road within a certain radius around your location is likely to struggle not just at multilevel intersections, but also with parallel roads and at regular intersections involving ways with different speed limits. It assumes that you know your location with an accuracy that you won't have available in many use cases, and fails in 3 dimensions because OpenStreetMap data does not contain altitude information, only a vertical ordering (i.e. whether an object is above or below another).
It seems to me that the problem you need to solve is finding out which road you're actually on. Once you know the road, you can easily access any of its attributes, including those relevant to speed limits.
This problem of finding the corresponding road for a location, and preferably a history of past locations, is called map matching. For OpenStreetMap data, I believe GraphHopper offers a map matching implementation and API.
Would storing time series data in a Knowledge Graph be a good idea ? What could be the benefits of doing so ?
It depends on the queries you want to do on the time series data, but I suspect the answer is NO.
Typical queries on time series data include the following:
moving averages; e.g. 30 day average of stock prices
median
accounting functions; e.g. average growth rate, amortization, internal rate of return and so on.
statistical functions; e.g. autocorrelation, and correlation between two series.
pattern finding; i.e. find a time series (or multiple time series) that has a similar pattern to this time series
In general time series data have a greater need for aggregation of a collection of data rather creating a graph of the data. This will likely cause any time series related queries to have poor performance on a graph like database.
A factor to consider is that the amount of data stored for time series can be way bigger than that for of a typical knowledge graph depending on the sample rate of the time series data.
Here are some of the references that brought me to this conclusion:
Indexing Strategies for Time Series Data
Demystifying Graph Databases - Analysis and Taxonomy of Data Organization, System Designs, and Graph Queries
I am looking for a convenient way to store and to query huge amount of meteorological data (few TB). More information about the type of data in the middle of the question.
Previously I was looking in the direction of MongoDB (I was using it for many of my own previous projects and feel comfortable dealing with it), but recently I found out about HDF5 data format. Reading about it, I found some similarities with Mongo:
HDF5 simplifies the file structure to include only two major types of
object: Datasets, which are multidimensional arrays of a homogenous
type Groups, which are container structures which can hold datasets
and other groups This results in a truly hierarchical, filesystem-like
data format. Metadata is stored in the form of user-defined, named
attributes attached to groups and datasets.
Which looks like arrays and embedded objects in Mongo and also it supports indices for querying the data.
Because it uses B-trees to index table objects, HDF5 works well for
time series data such as stock price series, network monitoring data,
and 3D meteorological data.
The data:
Specific region is divided into smaller squares. On the intersection of each one of the the sensor is located (a dot).
This sensor collects the following information every X minutes:
solar luminosity
wind location and speed
humidity
and so on (this information is mostly the same, sometimes a sensor does not collect all the information)
It also collects this for different height (0m, 10m, 25m). Not always the height will be the same. Also each sensor has some sort of metainformation:
name
lat, lng
is it in water, and many others
Giving this, I do not expect the size of one element to be bigger than 1Mb.
Also I have enough storage at one place to save all the data (so as far as I understood no sharding is required)
Operations with the data.
There are several ways I am going to interact with a data:
convert as store big amount of it: Few TB of data will be given to me as some point of time in netcdf format and I will need to store them (and it is relatively easy to convert it HDF5). Then, periodically smaller parts of data (1 Gb per week) will be provided and I have to add them to the storage. Just to highlight: I have enough storage to save all this data on one machine.
query the data. Often there is a need to query the data in a real-time. The most of often queries are: tell me the temperature of sensors from the specific region for a specific time, show me the data from a specific sensor for specific time, show me the wind for some region for a given time-range. Aggregated queries (what is the average temperature over the last two months) are highly unlikely. Here I think that Mongo is nicely suitable, but hdf5+pytables is an alternative.
perform some statistical analysis. Currently I do not know what exactly it would be, but I know that this should not be in a real time. So I was thinking that using hadoop with mongo might be a nice idea but hdf5 with R is a reasonable alternative.
I know that the questions about better approach are not encouraged, but I am looking for an advice of experienced users. If you have any questions, I would be glad to answer them and will appreciate your help.
P.S I reviewed some interesting discussions, similar to mine: hdf-forum, searching in hdf5, storing meteorological data
It's a difficult question and I am not sure if I can give a definite answer but I have experience with both HDF5/pyTables and some NoSQL databases.
Here are some thoughts.
HDF5 per se has no notion of index. It's only a hierarchical storage format that is well suited for multidimensional numeric data. It's possible to extend on top of HDF5 to implement an index (i.e. PyTables, HDF5 FastQuery) for the data.
HDF5 (unless you are using the MPI version) does not support concurrent write access (read access is possible).
HDF5 supports compression filters which can - unlike popular belief - make data access actually faster (however you have to think about proper chunk size which depends on the way you access the data).
HDF5 is no database. MongoDB has ACID properties, HDF5 doesn't (might be important).
There is a package (SciHadoop) that combines Hadoop and HDF5.
HDF5 makes it relatively easy to do out core computation (i.e. if the data is too big to fit into memory).
PyTables supports some fast "in kernel" computations directly in HDF5 using numexpr
I think your data generally is a good fit for storing in HDF5. You can also do statistical analysis either in R or via Numpy/Scipy.
But you can also think about a hybdrid aproach. Store the raw bulk data in HDF5 and use MongoDB for the meta-data or for caching specific values that are often used.
You can try SciDB if loading NetCDF/HDF5 into this array database is not a problem for you. Note that if your dataset is extremely large, the data loading phase will be very time consuming. I'm afraid this is a problem for all the databases. Anyway, SciDB also provides an R package, which should be able to support the analysis you need.
Alternatively, if you want to perform queries without transforming HDF5 into something else, you can use the product here: http://www.cse.ohio-state.edu/~wayi/papers/HDF5_SQL.pdf
Moreover, if you want to perform a selection query efficiently, you should use index; if you want to perform aggregation query in real time (in seconds), you can consider approximate aggregation. Our group has developed some products to support those functions.
In terms of statistical analysis, I think the answer depends on the complexity of your analysis. If all you need is to compute something like entropy or correlation coefficient, we have products to do it in real time. If the analysis is very complex and ad-hoc, you may consider SciHadoop or SciMATE, which can process scientific data in the MapReduce framework. However, I am not sure if SciHadoop currently can support HDF5 directly.
If we do the information visualization of documents, the graph generation across multiple documents often forms a mesh. Now to get a clear picture it is easy to form them with minimum data load and thus summarization is a good thing. But if the document load becomes
million then with summarization also the graph forms a big mesh.
I am bit perplexed how to clear the mesh. Reading and working round http://www.jerrytalton.net/research/Talton04SSMSA.report/Talton04SSMSA.pdf is not coming much help, as data is huge.
If any learned members may kindly help me out.
Regards,
SK
Are you talking about creating a graph or network of the documents? For example, you could have a network of documents linked by their citations, by having shared authors, by having the same terms appearing in them, etc. This isn't generally called a mesh problem, instead it is an automatic graph layout problem.
You need either better layout algorithms or to do some kind of clustering and reduction. There are many clustering algorithms you can use, for example Wakita & Tsurumi's:
Ken Wakita and Toshiyuki Tsurumi. 2007. Finding community structure in mega-scale social networks: [extended abstract]. Proc. 16th international conference on World Wide Web (WWW '07). 1275-1276. DOI=10.1145/1242572.1242805.
One that is particularly targeted at reducing complexity through "graph summarization" is Navlakha et al. 2008:
Saket Navlakha, Rajeev Rastogi, and Nisheeth Shrivastava. 2008. Graph summarization with bounded error. Proc. 2008 ACM SIGMOD international conference on Management of data (SIGMOD '08). 419-432. DOI=10.1145/1376616.1376661.
You could also check out my latest paper, which replaces common repeating patterns in the network with representative glyphs:
Dunne, C. & Shneiderman, B. 2013. Motif simplification: improving network visualization readability with fan, connector, and clique glyphs. Proc. 2013 SIGCHI Conference on Human Factors in Computing Systems (CHI '13). PDF.
Here's an example picture of the reduction possible: