Incremental crawling with Heritrix
Contents
1. 3 Lars R Clausen Concerning Etags and Datestamps Accessed May 2005 http www iwaw net 04 proceedings php f Clausen 4 Brian E Brewington and George Cybenko How dynamic is the Web WWW9 Computer Networks 33 16 257 276 2000 5 Marc Najork and Allan Heydon High Performance Web Crawling Accessed May 2005 ftp gatekeeper research compag com pub DEC SRC research reports SRC 173 pdf 12 Kristinn Sigur sson 6 Andrei Z Broder On the Resemblance and Containment of Documents IEEE SEQUENCES 97 pages 21 29 1998 7 John Frik Halse et al Heritrix developer documentation Accessed May 2005 http crawler archive org articles developer_manual html 8 Kristinn Sigurdsson et al Heritrix user manual Accessed May 2005 http crawler archive org articles user_manual html 9 Berkeley DB Java Edition Accessed May 2005 http www sleepycat com products je shtm 10 J Cho and H Garcia Molina The evolution of the web and implications for an incremental crawler In Proc of 26th Int Conf on Very Large Data Bases pages 117 128 2000 11 Mark Pilgrim What is RSS Accessed May 2005 http www xml com pub a 2002 12 18 dive into xml html 12 Internet Archive Accessed May 2005 http www archive org_ 13 Nedlib Factsheet Accessed February 2005 http www kb nI nedlib2. by assigning each URI a time of next processing when they are scheduled or return from being processed This time is based on the URIs assigned Incremental crawling with Heritrix 7 wait interval which in turn is based on the WaitEvaluator s prediction of when it will have changed again While our implementation of the queues is more cumbersome in terms of memory than the leaner snapshot frontier it does offer a simpler approach to maintaining the crawl s state and by extension makes the frontier s design less error prone It was immediately clear that to achieve this it would be necessary to use a much more flexible approach to storing data on disk than the custom written solution that the previous HostQueuesFrontier used We quickly narrowed the candidates down to the Berkley DB Java Edition 9 It being an embedded pure Java object oriented database seemed like the perfect candidate In fact it has proven itself to be quite apt to the task Interestingly enough parallel to the development of the ARFrontier the Internet Archive began developing a new Frontier to overcome many of the shortcomings of the HostQueuesFrontier that had shipped with version 1 0 0 This new Frontier The BdbFrontier was also based on the Berkley DB and support for it is now an integral part of the Heritrix framework The ChangeEvaluator relies on strict hashing actually performed automatically by the FetchHTTP processor when a document is fetched
3. newly discovered resources are inserted into the sequence While this would work it fails to take advantage of a critical fact the resources have widely varying probabilities of change within the same timeframe We need a way to predict how often a resource is likely to change in a given time span or rather how long we can wait before revisiting it and still be reasonably sure of not missing a version This calls for a set of heuristics that estimate the probability of change and using that estimate attempts to schedule resources for revisiting at the appropriate time To get an idea what these heuristics might use let us review what we know about resources on the internet and documents on the World Wide Web in particular It is worth noting quickly that the following lists for the most part general trends that while common enough to serve for heuristic evaluation do have many exceptions Firstly resources that change often are likely to continue to do so If we have observed that a document changes frequently it is probably safe to visit it frequently and vica versa Of course this offers no guarantee Websites may be updated sporadically A typical example would be the website of a politician During most of his elected tenure the website might only be updated once every few months However when elections are approaching the website is updated much more frequently possibly many times a day Despite this drawback most websites and res
4. omitting a single word Yet the fact that this word was deleted might be of considerable significance Unfortunately using HTTP headers does not do much to address this Dynamically generated content is both the most likely to have irrelevant constantly changing data and be missing useful header info Studies indicate that while HTTP headers are usually reliable in predicting change their usefulness in accurately predicting non change is much lower 3 Barring a remarkable improvements in the use of HTTP headers on the part of web servers it is doubtful that they will be of greater use than in the current implementation For this project we considered keeping track of the prediction made by the HTTP headers and compare them with the hash comparison The idea was to use this to build up a trust level Once a certain threshold is reached we would start using the header information to avoid downloads but still do occasional samples reducing the number of samples with rising trust levels Ultimately this was not done since it seems quite rare that they predict no change when a change has in fact occurred but adding this functionality would almost eliminate the chance of us failing to download a changed document because of faulty HTTP header information We have primarily focused on assessing change rates by looking at the documents history of change as observed by us However a significant amount of other factors could also be taken into
5. times are all configurable elements Furthermore Heritrix s overrides and refinements can be used to specify these values based on a variety of criteria Using the overrides 8 in Heritrix it is possible to override the settings of the WaitEvaluator on a per host basis Essentially each domain or host can have different initial wait time factors etc This allows the operator to pre adapt the crawl to known conditions In order to differentiate the settings based on the content type of documents several specialized WaitEvaluators are provided The specialized WaitEvaluators have the same settings but also have a configurable regular expression that must match documents content types The different WaitEvaluators can thus be stacked up in the processing chain Once a WaitEvaluator has handled a URI subsequent evaluators will pass on it At the bottom of the stack the generic WaitEvaluator then captures all file types not explicitly handled This is very similar to how the fetch and link extraction processors operate We then created a new Frontier dubbed the AdaptiveRevisitFrontier or ARFrontier for short While it relies like other frontiers on a series of per host based queues it implements them as priority queues rather than simple FIFO queues Additionally the queues have richer state control allowing them to be automatically suspended or in Heritrix jargon snoozed if no URI is ready for crawling This is accomplished
6. use of the other components 5 Unresolved and future issues Volatile change rates remain an issue Some papers 10 have suggested that in general document changes follow a Poisson process and that this could be used to create a better change prediction algorithm This remains to be seen but it is clear that there are web sites out there whose change rates vary significantly over the course of some time The strategy discussed here has tried to potentially capture all changes allowing the revisit intervals to drop down to as little as a second While that seems excessive many sites do change often each hour However the change rate may be heavily dependant on the time of day 4 Might it perhaps be best to use all visits from the last 24 hours to predict the next likely change One could take that further and suggest that all previous visits be taken into account The possibilities for analyzing such data and even cross referencing it with other documents within the same sites that may have a longer history for example or even While the existing snapshot frontiers do crawl prerequisite information such as robots txt and DNS lookups again at fixed intervals this is done by recreating the CrawlURIs 10 Kristinn Sigur sson from other sites with similar characteristics are almost endless Such analysis would be costly in terms of performance and currently Heritrix s architecture does not allow a processor handling one URI to
7. value has not changed If both indicators are found then they must agree that the document has not changed otherwise the filter assumes that it has changed This provides high reliability at the cost of some usefulness When both indicators are present you could somewhat increase their usefulness by only 3 The juggling of Frontier queues and maintaining their status has been one of the most problematic parts of the previous Heritrix frontiers 4 The FetchHTTP runs custom filters mid fetch after the HTTP headers have been downloaded and prior to the download of the content body Downloads of documents that do not match this filter are aborted 8 Kristinn Sigur sson downloading documents when both indicators predict change however this slightly reduces their reliability 3 and so we opted for the more reliable approach 4 Results Test crawls run with the software have gone quite well Depending on the initial and minimum wait settings as well as the politeness restrictions it is necessary to limit the number of URIs crawled on each host As expected if there are too many the software is unable to visit them in a timely fashion and their processing is constantly overdue While this will slowly correct itself on sites where most of the documents remain unchanged sites with a large number of frequently changing documents will not be as accommodating If the scope cannot be reduced then either the minimum time between visits must be i
8. Incremental crawling with Heritrix Kristinn Sigur sson National and University Library of Iceland Arngr msg tu 3 107 Reykjav k Iceland kristsi bok hi is Abstract The Heritrix web crawler aims to be the world s first open source extensible web scale archival quality web crawler It has however been limited in its crawling strategies to snapshot crawling This paper reports on work to add the ability to conduct incremental crawls to its capabilities We first discuss the concept of incremental crawling as opposed to snapshot crawling and then the possible ways to design an effective incremental strategy An overview is given of the implementation that we did its limits and strengths are discussed We then report on the results of initial experimentation with the new software which have gone well Finally we discuss issues that remain unresolved and possible future improvements 1 Introduction With an increasing number of parties interested in crawling the World Wide Web for a variety of reasons a number of different crawl types have emerged The development team at Internet Archive 12 responsible for the Heritrix web crawler have highlighted four distinct variations 1 broad focused continuous and experimental crawling Broad and focused crawls are in many ways similar the primary difference being that broad crawls emphasize capturing a large scope whereas focused crawling calls for a more complete coverage of a smaller
9. While generally acceptable there are known instances of irrelevant data that changes with each visit To address this we created another processor HTTPContentDigest It is inserted into the processing chain between the fetch processor and the ChangeEvaluator An operator enters a regular expression matching sections in documents that should be omitted The processor then recalculates the hash ignoring any sections that match the regular expression for documents that it runs on While costly it is only intended to be used against specific known issues By using overrides and refinements an operator can create a crawl order that only applies this processor on URIs that are known to be troublesome It is thus possible to apply different regular expression to each issue and only to the documents that contain the problematic section or are very likely to do so On another front we wanted to take advantage of HTTP header information We decided to create a filter that the HTTP fetcher processor could run mid fetch to evaluate if the download should be aborted because the document had not changed since the last visit The rather cumbersomely named HTTPMidFetchUnhangedFilter checks both the last modified timestamp and the etag value and compares it to the results of the most previous crawl of the URI If neither is present the filter can of course not do anything If only one is present the filter will determine that the document is unchanged if that
10. access meta data stored for another URI These are however implementation issues and if an algorithm were devised that made use of such information to more accurately predict a document s change rate they would probably be worth overcoming Change detection also remains to be tackled We provided a simple strict comparison coupled with the ability to selectively overlook section of documents While this works most of the time it may not be suitable for large incremental crawls and can be a bit unwieldy as well This is actually a very large topic and a comprehensive overview is beyond the scope of this article We ll simply state that the better we are able to detect changes in content and separate those changes from changes in layout the more effective any adaptive revisiting policy will become But that still won t address such issues as say an archive document containing a section with current front page story The current front page story is still content of sorts it just isn t of any worth in the current context Close enough comparison algorithms 6 may overcome this to some extent but may cause us to overlook minor changes in the actual content This minor in terms of the amount of bytes affected change might however be considered a significant change in relation to the content For example a company or other party might amend a press release on their website to tone down the headline This might be as small a change as
11. account As discussed earlier the documents position in relation to the front page or seed can be considered Alternatively or additionally a ranking system of some sort could be used to evaluate a documents relative worth Looking even further when crawling sites offering RSS feeds 11 or other similar services we could use them as triggers for revisiting certain sections of the webs as well as the presumably new stories being advertised via the feeds Obviously not all Incremental crawling with Heritrix 11 sites offer such feeds nor are all changes reported by them but they would allow us to capture all major changes accurately assuming that the feed is well handled On a more immediate and practical note Heritrix s statistics are still rather snapshot oriented Especially those provided by the web user interface We took advantage of a feature of the crawl log that allows custom modules to append arbitrary data to each URI s line This means that it contains such information as the wait time how overdue it is and the number of visits to a URI In addition the frontier report that is accessible via the web GUI contains some highly useful state information The progress statistics are however less accommodating and do not differentiate between multiple visits to the same URI and visits to different URIs properly This is especially notable in the console progress bar in the web GUI that will never reach 100 because a continuo
12. e bandwidth more effectively and thus limit the stress our harvesting places on web servers It is worth noting however that while doing this we also achieve a secondary benefit in reduced storage costs since duplicate data need not be stored That however brings us to another problem detecting change One of the key aspects discussed above relies on being able to detect if a change has occurred between the times when a resource is visited Doing a simple strict hash comparison does achieve this to a degree It certainly detects any change and reduces the odds that we discard a changed document to nearly zero Unfortunately it relies on a bit by bit comparison and takes no notice of the actual content Thus any change Incremental crawling with Heritrix 5 will be detected even something as utterly trivial as a clock feature on a web site changing every second The problem lies in the dynamic content generation of web servers While normally any change would be of interest automatic changes that do not relate to the content but rather time or some other factor are not necessarily of interest We are primarily interested in changes to the content A good deal of work has been done on the subject of detecting documents that are roughly the same 6 for example outlines a shingling method for evaluation saying basically that if enough shingles are the same for two documents they are the same While such approximations may be valid m
13. e used by the National and University Library of Iceland to augment the regular snapshot crawls of the is domain A selection of thirty to forty web sites containing news political discussions and other similarly valuable content will be crawled continuously The exact parameters as far as minimum wait times etc are still being developed We are continuing to experiment with the software evaluating what are good wait intervals how well the content prediction works etc This work will likely continue for some time A considerable amount of works does remains both further testing the software and also in experimenting with the configurations to improve the incremental behavior Not to mention testing possible alternative wait estimation algorithms gt The valence of a host dictates how many simultaneous request Heritrix can make to that host Generally a valence of 1 is recommended to avoid hammering a site Incremental crawling with Heritrix 9 Finally the AR module became a part of the standard Heritrix release as of version 1 4 0 The AR work has led to several mostly minor changes in the Heritrix framework to better accommodate incremental crawling This was done without adversely affecting snapshot strategies and serves to strengthen Heritrix s overall flexibility Primarily these changes focus on the Craw URI object that wraps the URI with other essential data It is passed from the Frontier to the processors and back and rep
14. ent state information of the URIs being passed through the processing chain passing over unchanged documents or otherwise treating them in an appropriate manner Ultimately this was not done because of a desire to minimize changes to the Heritrix framework Instead the content state is stored in the CrawlURI s hash map Hopefully this will be revisited in the future The WaitEvaluator runs in the post processing section of the URI processing chain A wait interval is assigned to each URI between fetches When a URI reaches the WaitEvaluator it examines this value and calculates a new one based on whether or not the document had changed between visits If the wait interval is missing a default initial value is assigned That should only happen when a URI is processed for the first time URIs that cannot be evaluated for change however are assigned a fixed and permanent wait interval Currently this applies only to DNS lookups A simple strategy for estimating a new wait interval is used If a document has changed its wait interval is divided by a constant Similarly if it is unchanged it is multiplied by a constant By default only the previous wait interval is considered However odds are that URIs will be somewhat overdue possibly very overdue and thus there is an optional feature to use the actual time that has elapsed between visits in determining the new wait time The factors as well as the initial wait time and maximum and minimum wait
15. es such as Word and PDF documents are also less likely to change than pure text files There is a certain amount of connection between the size of a resource and its change rate As the size increases its likely change rate goes down This assumes that image audio video and formatted files are generally larger than pure text documents While not always true this generally holds We are therefore able to use this information not only to reduce the number of visits we must make to a certain part of our scope but to one of the most bandwidth intensive part of our scope Another factor that might be taken into consideration is a document s location in a web site s hierarchy Generally the front page of a web site changes more often than any given page in the archive where changes may never occur Assuming that the initial seeds are the front pages of each site it would be possible to use the depth from seed as a factor in deciding the initial wait time Finally one could conceivably do some form of contextual analysis on the resource s content or metadata to evaluate the probable change rate For example 4 observes that documents without the last modified information are about twice as likely to change between two visits as those that have it There are undoubtedly many other possible indicators that could be used but covering them all is well outside the scope of this article The primary goal in using incremental crawling is to to utiliz
16. hot strategy can be described as choosing between space and time completeness Naturally we would wish to capture both well and with no hardware limitations we might do so but in light of limited bandwidth and storage difficult decisions must to be made As noted above Heritrix already implements a snapshot strategy for its focused crawls While work continues to expand its capabilities in order to scale up to truly broad crawls the need for an incremental strategy also needs to be addressed This led us to implementing a generic incremental strategy for Heritrix One that would both serve as a proof of concept that the Heritrix framework could in fact support an incremental strategy and would provide the basic building blocks for tailoring such crawls Several approaches to creating an incremental crawler have been either implemented or proposed 10 13 5 none of them takes the Heritrix framework into account As a result we have chosen to look at the basic requirements of continuous crawling and what knowledge we have of the way documents change on the World Wide Web to develop an appropriate strategy for Heritrix 2 URIs Uniform Resource Identifiers are a superset of the more familiar URLs Heritrix while supporting only URLs uses the more general term URI for future proofing 7 Incremental crawling with Heritrix 3 What we wanted to create is an addition to Heritrix that allows for incremental crawling while both maintaini
17. ncreased this can be done selectively so that key documents are visited more often or the politeness restrictions loosened The ARFrontier supports host valence higher than one allowing for multiple connections the minimum wait time between connections can also be reduced to crawl more aggressively An experimental feature for valence higher than 1 was provided in the snapshot frontier that shipped with Heritrix 1 0 0 This has not been included in the newer BdbFrontier due to the added complexity The ARFrontier s state rich host queue system however allowed us to add this feature without undue complications While the number of URIs per host is somewhat limited by these factors the overall number of URIs is not as limited A certain amount of memory overhead is incurred for each new host that is added but it is still feasible to crawl up to thousands of hosts although this has not been fully tested yet and potentially as many as millions of URIs at once However as the number of hosts increase they start blocking each other Depending on the hardware Heritrix may be able to crawl as many as 50 60 documents a second Once you have enough hosts to keep it fully occupied despite politeness restrictions a backlog can occur whereas if the number of hosts or URIs is too small to fully occupy the available resources URIs will only become overdue because their host is overworked Following these successful initial test the software will b
18. ng Heritrix s inherent modularity and not compromising the efficiency of its snapshot crawling Making it possible to swap out appropriate portions of the logic by substituting certain modules Basically provide the building blocks for an incremental strategy with Heritrix and one solid implementation of it We have focused on adaptive techniques as a way to address the extreme variety found on the Internet While mostly heuristic in nature we believe that an adaptive approach can make much better use of available resources In the following sections we ll start by looking at what we know of the Web that can help shape possible strategies for incremental crawling We then continue to describe how we implemented one variation of those for Heritrix and discuss briefly our experiences with the resulting experimental features Finally we ll outline some of the main challenges that still lie ahead 2 Incremental strategy The basic goal of any incremental strategy is to capture every change in all the resources within its scope Clearly unless the scope is extremely limited this cannot be achieved since it would require a constant flurry of requests to the server to check for changes and even if you did have the resources to do that it would place an excessive strain on the server One obvious strategy here is to crawl the entire scope then start again and replay the crawl Resources that disappear between iterations are discarded while
19. ost of the time it is questionable to employ them without first examining how they handle the documents within the intended scope of any given crawl An alternative idea is to provide the operator with the ability to selectively weaken the strict hash This could be done by specifying regular expressions and omitting the sections of documents that match them when creating a hash While costly in terms of processor power it allows the operator to tackle known issues While one might argue that just tackling known issues is not enough when crawling the entire web it is worth remembering that continuous crawls are more likely to be focused on a carefully selected and in comparison to broad crawls small number of web sites This method also has the advantage of always erring on the side of caution 3 Implementing a strategy When developing an addition to the Heritrix crawler two primary aspects need to be addressed The first is the so called Frontier A frontier is a pluggable class that maintains the state of the crawl The default frontier implements a snapshot strategy where it is assumed that URIs are to be crawled once and once only Clearly this won t work The other important aspect is the chain of processors When the Frontier issues a URI for crawling it passes through a series of processors Each processor is a pluggable class and there is no limit to the total number of processors so new ones can be added Typically each proce
20. ount of time a snapshot crawl will fail to detect unchanged documents leading to needless duplicates Snapshot crawling is therefore primarily of use for large scale crawling that is either crawling a large number of websites or trying to crawl each website completely leading to very deep crawls or both Continuous crawling requires the crawler to revisit the same resources at certain intervals This means that the crawler must retain detailed state information and via some intelligent control reschedule resources that have already been processed Here an incremental strategy is called for rather than the snapshot strategy used in broad and focused crawls An incremental strategy maintains a record of each resource s history this is used in turn to determine its ordering in a priority queue of resources waiting to be fetched Using adaptive revisiting techniques it is possible to capture the changes in online resources within its scope far more accurately in turn allowing the crawler to revisit each page more often In addition revisits are not bound by crawling cycles as with the snapshot approach since any page can come up for revisiting at any time rather than only once during a run of the crawler However because of additional overhead and because of the need to revisit resources an incremental strategy is unable to cope with as broad a scope as a snapshot crawl might To sum things up the choice between an incremental or snaps
21. ources are believed to have reasonably regular change rates After all a web site that wishes to attract regular 4 Kristinn Sigurdsson readers needs to be consistent in its presentation of new material As noted before this is not an absolute and further research is needed to establish just how much an issue erratic change rates in web pages are Secondly the file type of resources or documents significantly affects the probable change rates 2 While changing HTML or other simple text files is both easy and straight forward not to mention expected by the user in many cases and frequently handled by content management systems changing images is more complex In fact browsers and proxies will generally cache images while HTML pages are requested for each page view Images and other multimedia files are usually not generated on demand unlike many HTML files and when a new or changed graphic is needed it is customary to add rather than replace an image This allows us to predict different initial wait times between revisits before we gather any further information that enables us to adapt to observed change rates HTML and other pure text documents can be assigned a relatively short initial wait while a longer initial wait time is assigned to image files Higher bandwidth media such as audio and video files are even less likely to change due to their complex nature and can thus be assigned even higher initial wait times Formatted text fil
22. resents effectively both the URI and its state For snapshot crawling this object could be discarded at the end of the processing cycle An incremental crawler does not have this luxury but it was not just a simple matter of keeping it around Some of the data by its very nature must be reset before each processing round In fact since the CrawlURI offers a generic keyed hash table where frontiers and processors can store any arbitrary information it was possible that a processor might store a non serializable object there effectively crashing the crawl With the AR module now officially released as a part of Heritrix we hope that others will use it and provide feedback both in the form of comments and suggestions and also by contributing code This was solved by redesigning the CrawlURI to separate the data into transient and persistent data leaving the responsibility of accurately declaring the data to the processors and other modules Data that is common to all CrawlURIs was similarly divided and frontiers need only call a post processing cleanup method once they are ready to insert them back into the queues While this new addition to Heritrix does not solve every issue with incremental crawling it does provide the framework for conducting such crawls Having taken full advantage of Heritrix modular nature each aspect of the crawling strategy be it content comparison change prediction etc can be customized while still making full
23. scope Both approaches use a snapshot strategy which involves crawling the scope once and once only Of course crawls are repeatable but only by starting again from the seeds No information from past crawls is used in new ones except possibly some changes to the configuration made by the operator to avoid crawler traps etc The snapshot strategy sometimes referred to as periodic crawling 10 is useful for large scale crawls in that it minimizes the amount of state information that needs to be stored at any one time Once a resource has been collected the crawler need 1 In the context of web crawling scope refers to the set of URIs that are by some virtue a desired part of the crawl That is if encountered they should be crawled This is also sometimes refered to as a segment 2 Kristinn Sigurdsson only store a fingerprint of the URIs and data structures are also simplified since first in first out queues can be used to hold discovered resources until they can be processed This makes it possible to crawl a fairly large scope using a snapshot approach and initially this is what Heritrix did As of version 1 0 0 it offered decent capabilities for focused crawls and work has continued on expanding its capabilities to cope with larger crawls However this does not do a good job of capturing changes in resources Large crawls take time meaning that there is a significant gap between revisits Even if crawled within a reasonable am
24. ssor does a discrete unit of work like fetching the document over the internet performing link extraction or writing the contents to disk This provides a modular system allowing us to make full use of existing processors while adding a few extra that are needed to manage a continuous crawl We created two key processors the ChangeEvaluator and the WaitEvaluator The ChangeEvaluator runs right after a document has been fetched It decides if the document has changed or not based on a hash comparison more on that later Changed documents are marked appropriately and move on down the processor chain whereas the processing of unchanged documents is preempted and they are sent to the post processing portion for completion It is still regarded as a success but we skip over link extraction that is of no value if a document has not changed 6 Kristinn Sigur sson This preemption also bypasses the writing phase of the processor chain While this is acceptable with current ARC formats since they do not allow records stating that a duplicate was found in the future that may change To facilitate this a content state could be added to the CrawlURI object and thus to the Heritrix framework API The CrawlURI objects do allow any arbitrary data to be stored and retrieved in a hash map This is sufficient for most data however it might be argued that the content state was of a more general nature In the future processors should respect the cont
25. us frontier always has more stuff to crawl Some discussions have taken place about allowing pluggable web pages and controls in Heritrix but until something along those lines is provided accurate statistics will be slightly problematic This is most noticeable in monitoring a crawl but we do not feel that this causes any problem in post crawl analysis where the crawl log can be analyzed 6 Parting words Heritrix aimed to provide a good general platform for web crawling and therefore we have aimed to provide a good general platform for continuous web crawling within that framework We are aware that it will not solve every issue but we are happy to say that it meets our initial criteria and we can only hope that as others try it out and add their improvements and alternative implementations that the AR addition will further strengthen Heritrix Finally a word of thanks to Gordon Mohr and Michael Stack of the Internet Archive for their assistance in the development effort orsteinn Hallgrimsson of the National and University Library of Iceland also deserves a big thank you as without him this project would not have been possible Lastly a thank you to the Internal Internet Preservation Consortium for their generous support References 1 Gordon Mohr et al Introduction to Heritrix Accessed May 2005 http www iwaw net 04 proceedings php f Mohr 2 Andy Boyko Characterizing Change in Web Archiving Internal IIPC document unpublished
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