see freshly deprecated https://developers.google.com/webmasters/ajax-crawling/
Implementation improves parsing of the homepage (ajax page) which uses metatag "fragment" in header and parses supplied html snapshot instead of mostly empty ajax/scripted page.
Implementation supports also hash-bang urls (url with anchor starting with ! like ...path#!hashfragment) but our crawler filters it
(use of hash-bang is controversly discussed and proposal is deprecated, makes no sense to adjust the crawler, but as long as it is used by some sites the minor change/improvement in htmlparser is good for some time).
Quick - how does it work
- if metatag fragment with content "!" is found
- htmlparser tries to get content of htmls snapshot (using a different url)
- htmlparser returns 2 documents (original url and snapshot content - but using same original url)
- after parsing result documents are joined (and stored to index containing content also from snapshot page... as the original ajax page contains typically no parseable html content)
Reads document level included title and description and skips the graphic content to save bandwidth.
svg metadata element is not interpreted
- remove rdfParser from init (current function identical with genericParser)
to prevent blank thumbnail display in image search because of not handled source which don't load on click.
Now the cross icon indicates the problem (inlcuding not supported format)
bayesian filters. This can be used to classify documents during
indexing-time using a pre-definied bayesian filter.
New wordings:
- a context is a class where different categories are possible. The
context name is equal to a facet name.
- a category is a facet type within a facet navigation. Each context
must have several categories, at least one custom name (things you want
to discover) and one with the exact name "negative".
To use this, you must do:
- for each context, you must create a directory within
DATA/CLASSIFICATION with the name of the context (the facet name)
- within each context directory, you must create text files with one
document each per line for every categroy. One of these categories MUST
have the name 'negative.txt'.
Then, each new document is classified to match within one of the given
categories for each context.
during surrogate reading: those attributes from the dump are removed
during the import process and replaced by new detected attributes
according to the setting of the YaCy peer.
This may cause that all such attributes are removed if the importing
peer has no synonyms and/or no vocabularies defined.
reason: experimental implementatin of RDFa parser not executed (limited to special urls) but may cause error on normal html parsing due to a inputstream.reset
keeping surrogates after processing is essential for some users. If the
space they are taking is too high, please set up an automatic deletion
process (like a cronjob).
to support the new time parser and search functions in YaCy a high
precision detection of date and time on the day is necessary. That
requires that the time zone of the document content and the time zone of
the user, doing a search, is detected. The time zone of the search
request is done automatically using the browsers time zone offset which
is delivered to the search request automatically and invisible to the
user. The time zone for the content of web pages cannot be detected
automatically and must be an attribute of crawl starts. The advanced
crawl start now provides an input field to set the time zone in minutes
as an offset number. All parsers must get a time zone offset passed, so
this required the change of the parser java api. A lot of other changes
had been made which corrects the wrong handling of dates in YaCy which
was to add a correction based on the time zone of the server. Now no
correction is added and all dates in YaCy are UTC/GMT time zone, a
normalized time zone for all peers.
- date navigation
The date is taken from the CONTENT of the documents / web pages, NOT
from a date submitted in the context of metadata (i.e. http header or
html head form). This makes it possible to search for documents in the
future, i.e. when documents contain event descriptions for future
events.
The date is written to an index field which is now enabled by default.
All documents are scanned for contained date mentions.
To visualize the dates for a specific search results, a histogram
showing the number of documents for each day is displayed. To render
these histograms the morris.js library is used. Morris.js requires also
raphael.js which is now also integrated in YaCy.
The histogram is now also displayed in the index browser by default.
To select a specific range from a search result, the following modifiers
had been introduced:
from:<date>
to:<date>
These modifiers can be used separately (i.e. only 'from' or only 'to')
to describe an open interval or combined to have a closed interval. Both
dates are inclusive. To select a specific single date only, use the
'to:' - modifier.
The histogram shows blue and green lines; the green lines denot weekend
days (saturday and sunday).
Clicking on bars in the histogram has the following reaction:
1st click: add a from:<date> modifier for the date of the bar
2nd click: add a to:<date> modifier for the date of the bar
3rd click: remove from and date modifier and set a on:<date> for the bar
When the on:<date> modifier is used, the histogram shows an unlimited
time period. This makes it possible to click again (4th click) which is
then interpreted as a 1st click again (sets a from modifier).
The display feature is NOT switched on by default; to switch it on use
the /ConfigSearchPage_p.html servlet.
given css class and extends a given vocabulary with a term consisting
with the text content of the html class tag. Additionally, the term is
included into the semantic facet of the document. This allows the
creation of faceted search to documents without the pre-creation of
vocabularies; instead, the vocabulary is created on-the-fly, possibly
for use in other crawls. If any of the term scraping for a specific
vocabulary is successful on a document, this vocabulary is excluded for
auto-annotation on the page.
To use this feature, do the following:
- create a vocabulary on /Vocabulary_p.html (if not existent)
- in /CrawlStartExpert.html you will now see the vocabularies as column
in a table. The second column provides text fields where you can name
the class of html entities where the literal of the corresponding
vocabulary shall be scraped out
- when doing a search, you will see the content of the scraped fields in
a navigation facet for the given vocabulary
trace: java.lang.OutOfMemoryError: Java heap space
at java.awt.image.DataBufferInt.<init>(DataBufferInt.java:75)
at java.awt.image.Raster.createPackedRaster(Raster.java:467)
at java.awt.image.DirectColorModel.createCompatibleWritableRaster(DirectColorModel.java:1032)
at java.awt.image.BufferedImage.<init>(BufferedImage.java:331)
at net.yacy.document.parser.images.bmpParser$IMAGEMAP.<init>(bmpParser.java:149)
at net.yacy.document.parser.images.bmpParser.parse(bmpParser.java:69)
at net.yacy.document.parser.images.genericImageParser.parse(genericImageParser.java:116)
reger