2.1.3 Hold the title of a News Item 2.1.4 Describe the subject of a News Item 2.1.5 Describe the classification (genre) of a News Item (e.g. 'Sports news', 'Weather forecast') 2.1.6 Hold information about location covered by the News Item 2.1.7 Hold an abstract (synopsis) of the News Item 2.1.8 Hold person related data, such as forename. IFlicks 2.4 review: A valuable tool for adding valuable metadata to digital video files Use iFlicks to add metadata, convert video files, and add videos to your iTunes library. IFlicks 3.2.1; iFlicks 3.2; iFlicks 3.0.4; iFlicks 3.0.3; See all 46 articles. Wide / Square / Poster Artwork; Loading Metadata from mp4/m4v Files or iTunes tracks; Movie Metadata Lookup; TV Show Metadata Lookup; Changing Artwork; Metadata for TV Show Specials; See all 7 articles. The works perfectly for all H.264, HEVC, AAC, AC-3 and EAC-3 streams. Your video content is not H.264 or HEVC (H.265)? IFlicks uses the most advanced encoders to convert your video stream while keeping quality high and files sizes low. IFlicks even utilizes hardware encoders like the Apple T2 Chip when available to reduce the time. The app has been removed from the mobile store. Jendrik Bertram. Video Add to dashboard. Get widget Add keyword × Add new keyword for tracking Close Track keyword What is MetricsCat.
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- Iflicks 3 Metadata And Conversion 3 2 12
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I’ve just recently decided to make the switch from Kodi on my Home Theatre PC to AppleTV 3. Clearly, we’re an Apple household and the Kodi HTPC was a bit of an odd-one-out as it was running Mythbuntu. I’d been considering the switch on power usage grounds (the Apple TV uses a measly 1.5Watts of electricity – and around 0.7Watts when idle, which is somewhere close to 30 to 60 times less than the HTPC and then Kodi had a crash and ate all my hard work with customizations and the wife had a bit of a meltdown…
But Apple TV doesn’t play MKV files natively (and definitely doesn’t support AVI) as all the files have to be stored in iTunes on a Mac. This means I have terabytes of movies, TV Shows and Home Videos to convert from MKV/AVI format. Plus, all the metadata is in Kodi format .nfo files which are completely irrelevant to iTunes. I’ve spent ages customizing my library with all the information from online sources making the library work for me and I really didn’t want to lose all that.
I tried a number of solutions before settling on iFlicks 2 (VideoDrive came the closest and I’ll review that later, with solutions such as WonderShare Video Converter Ultimate looking like a decent option too). Handbrake and iDentity would’ve worked but didn’t provide an automated solution by any stretch of the imagination, and bulk imports were quite difficult with HandBrake and iDentity (and it was very slow).
iFlicks 2 meets all my requirements. It supports bulk operations, it looks up the metadata online (I think from themoviedb.com and thetvdb.com) and is extremely intelligent in it’s lookup plus it operates on bulk imports. So, lets look at it a little closer;
iFlicks 2 Preferences are the place to start here – and iFlicks keeps this as simple as possible. It offers a number of presets for the video conversions and my requirements are pretty simple – keep my video at the same resolution and quality as they previously were but make them iTunes compatible. Being iTunes compatible means they’re Apple TV 3 compatible too. And the bonus with this preset is that if the file is an MKV then it probably just needs a bit of tweaking rather than a full recode – which is extremely fast. In the screenshot to the right you’ll notice that I haven’t checked the ‘Optimize for Streaming’ choice, as the iFlicks 2 website support FAQ suggests this isn’t necessary for over the LAN streaming to an Apple TV and it causes the whole file to be rewritten again, adding time to the conversion that in my case isn’t necessary.
iFlicks 2 will automatically add the converted video file to iTunes – although in my case I have de-selected that option as I am converting on one machine with iTunes on a separate machine and iFlicks is putting the converted file into the ‘Automatically Add To iTunes’ folder instead. This works well (although I found with ‘Optimise for streaming’ set on, each file was being added to iTunes twice). You can also set the file to be deleted once converted – useful if you’re low on disk space or converting onto the same disk. The final two options are localisation options – and the ratings options I didn’t find in any of the alternatives at all. This option allows you to choose which countries ratings to look up for your movies and shows.
Watch Folders are supported so that if a video appears in a folder that iFlicks has been told to watch then conversion begins automatically – very handy if you’re using any kind of automation such as a DVR shared on the network or SickBeard or similar utility. Having said that, I couldn’t get iFlicks to actually pick up any of the files in my Watch Folder but I haven’t played with it too much in that respect yet so I’m probably doing something wrong.
One of iFlicks 2’s strongest features is the Rules you can apply to movies. iFlicks 2 will apply rules to your conversions at 3 distinct stages during the process. The first is when the file is loaded, and it comes with 3 pre-defined rules here which are to apply the necessary tags for HD-720, HD-1080 and to rename the file into proper format for season and episode. After Metadata Download and after Processing can also have rules applied to them and these can be built in processes, or you can call your own personal AppleScripts to ‘post-process’ the converted files.
App pier 1 3 download free. Converting the files is as simple as dragging and dropping your chosen files from Finder onto the main window (see right – click the image for a larger, clearer version). iFlicks will automatically attempt to look up the metadata for your video file based on its filename and this is extremely intelligent and has found about 98% of files I’ve thrown at it. So long as your file is named appropriately there’s no reason it should fail – unless no-one has put the data into the online sources in the first place. And this is an area that iFlicks 2 absolutely excels when compared to the competition – if it’s unsure that it’s found a match, but has various options it thinks it could be, it’ll popup a ‘search’ box which gives you the option to edit the video title and type (ie, movie, tv show) and it will show you a list of the options it’s already found, enabling you to choose the one you want to use. This aspect of iFlicks 2 is without doubt the best part of the whole software.
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Conversion of the files into an iTunes compatible format varies between fast and extremely fast. MKV files often already contain iTunes compatible H.264 video tracks, they just need to be rejigged, so this should be extremely quick – and is. But I’ve also found converting AVIs to be extremely quick (not AS quick, but still way quicker than HandBrake).
iFlicks 2 will also lookup chapter markers and include them (or use ones that are already in the original file), as well as of course downloading Movie or TV Show artwork (and give you the option to change it if you don’t like the particular one it’s chosen).
All in all, iFlicks 2 is everything I ever wanted Kodi to do (and to be fair it mostly did) with it’s scrapers – with some additional benefits, such as running as a GUI on Mac, with (and this is a big winner) a GUI way of choosing which data to look up and being able to correct it easily without the need to write complicated XML .nfo files (something my wife and mother-in-law were never going to learn!)
Support from the author has been superb and very swift. This is one of the only apps to date on ReviewMacSoftware that has a 10/10 rating. This product is worth every cent if you’re manipulating movie or TV Shows onto your Apple TV via iTunes, I seriously cannot recommend it high enough.
Further Information can be found at http://www.iflicksapp.com/en
Downloads are via the Mac App Store at https://itunes.apple.com/app/id731062389?mt=12
Downloads are via the Mac App Store at https://itunes.apple.com/app/id731062389?mt=12
- 10/10Design - 10/10
- 10/10Features - 10/10
- 10/10Cost - 10/10
- 10/10Ease Of Use - 10/10
- 10/10Customer Support - 10/10
- 10/10Overall Value - 10/10
Summary
Positives: Fast, easy to use, well designed, flexible, online lookups, very reliable.
Negatives: None. Seriously.
Price: $24.99 from the Mac App Store
Trial Available: No. But trust us, if you’re converting videos for use on iTunes/Apple TV this software is the best.
Negatives: None. Seriously.
Price: $24.99 from the Mac App Store
Trial Available: No. But trust us, if you’re converting videos for use on iTunes/Apple TV this software is the best.
Website: http://www.iflicksapp.com/en
Download: https://itunes.apple.com/app/id731062389?mt=12
Download: https://itunes.apple.com/app/id731062389?mt=12
User Review
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This page has no particular status within the working group.
- 1Mapping of Web Tabular Data to RDF
- 2Design Goals
- 3Conversion Process
- 5Alternatives
- 6Annotations
Mapping of Web Tabular Data to RDF
This note describes producing RDF from CSV files. The CSV files are assumed to conform to the'Model for Tabular Data and Metadata on the Web'.
That document defines 3 kinds of data model for tabular web data.
- Core Data Model: defines the notion of table by defining the regular structure in the CSV file, such as having a header row and each row having the same number of columns.
- Annotated Data Model: adds additional metadata about the file and may include the expected types of values in columns and whether a column can be used as a unique key.
- Grouped Data Model: covers a collection of tables.
@@status: currently not considering groups of tables in this note.
This note describes a process for the production of RDF from such tabular data. It works in the absence of additional metadata (Core Data Model level) and canproduce more useful RDF if annotation is present, for example creating datatypexsd:date for data information, instead of simple strings.
The format and definition of the annotation metadata is not define din this note; it is a separate product of the working group and will apply to several differentconversions to RDF, JSON and XML.
Context -- RDB2RDF
Previous work in mapping relational databases to RDF resulted in an automatic mapping,using only database schema information and also mapping based on a description of thetranslation:
- 'R2RML: RDB to RDF Mapping Language'
- 'A Direct Mapping of Relational Data to RDF '.
Design Goals
(@@needs more)
- Output should be streamable, that is, CSV files can be processed without needing to rad the whole file first.
- Capture all details of the data model
- Possible to perform much of the translation with text processing tools, not presuming a full RDF toolkit.
![Conversion Conversion](https://www.iflicksapp.com/img/screenshots/screenshot-metadata-dark.png)
Issues
- RFC 7111 fragment identifiers.
- Should a CSV frgement refer to something in the RDF?
- If so, rows might be be <http://host/data.csv#row=1>.
- Balance of complexity.Here or assume other prcoessing.
- Extracting different entities from a row (different subjects within a row)
- Entities across rows
Conversion Process
Defined here for when there is no additional annotation.
Outline
Each row is used to produce a number of RDF triples all with the same subject.Each column has it's own predicate. Row numbering information is included.
- Read annotations, if any.
- Process headers.
- Process each row.
- Decide subject RDF term
- Process each cell to create RDF terms (literals or URIs)
- Create triples with the row subject, column predicate and cell value.
This process is row-oriented.
Process the header row
Each column name is used to producea predicate URI. This formed from the URL of the CSV file, a # to startthe URI fragment and name of the column.
Where possible the exact name of the column should be used but that is notalways possible. A standard conversion algorithm is needed.
- Spaces becoming _
- Other characters, like /, % or % become %-encoded.
In standard 'HTML Form URL encoding' (application/x-www-form-urlencoded), spaces become + but _ may be more natural for CSV data.
The Annotated Data Model recognizes that column order can be important so column number is included in the output.
Process a row
The cell value datatype is determined by the column annotation if present.This may include the (human) language for the column.
If no annotation is available, then the datatype of the cell is determinedby looking at CSV entry:
- integers - optional sign, then digits 0-9
- decimal - optional sign, then digits 0-9, decimal point, digits 0-9
- xsd:double - exponent present.
- otherwise, it is a string.
(@@ align choices with user expectations)
Example
For concrete example, we mostly use Turtle syntax.This makes it easier to see the RDF triples.
The CSV file is assumed to come from web location<http://host/data.csv>.
This is a CSV file of 2 columns, with 1 header row and 2 data rows.
In the absence of any annotations, each data row is given a different blank node.
Each row of the file generates a set of RDF triples, using the column information todetermine the predicate. Triples for a given row have the same subject. In addition,row number information is added. This is kept separate from the particularCSV file column names by using a different URI to base URI generation on.
For the example data above, this would produce:
Town names are strings and the population cells are numbers.
This data could be queried with SPARQL:
Without the bnode abbreviations:
In N-Triples:
Choice of syntax is not defined in this note.
These three examples are the same RDF. Blank node label have no significance to machines.
Alternatives
Iflicks 3 Metadata And Conversion 3 2 12
RDF lists provide a different way to retain ordering information. They are not easy to work within RDF tripe stores due to little native support in general.
For example, the order of columns of retained by metadata in the RDF generated:
Notes
- The CSV file may change over time - the predicate will be the same if the column names are the same even if their meaning is different. Whether this is a bug or a feature is a matter of opinion.
- Columns names starting with a digit can lead to RDF/XML unfriendly predicates.
- This process can be done by text processing for the case of CSV to Turtle. An RDF tool kit is not required.
Annotations
Additional annotations on the data may be used to:
- check and choose the data type for a column, e.g. make all number xsd:double.
- Provide a template for generating subject URIs, not using blank nodes.
- One column might be used to
Column datatyping
Subject URIs
Instead of a blank node, a generated URI could be used:
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giving data rows:
There still might be a need to include csv:row depending on the importance of row order to avoidmicroparsing URIs.
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Metadata about conversion
Metadata for the conversion could (optionally) be added:
Table Groups
@requirements required
@@ need to create links by URI
Retrieved from 'https://www.w3.org/2013/csvw/wiki/index.php?title=CSV2RDF&oldid=246'