Project Progress

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0cm;mso-list:l1 level1 lfo1;tab-stops:36.0pt"><#comment></#comment>1. <#comment></#comment>Introduction<O></O>

PingER has a huge amount of data and, until the conclusion of this project, the easiest way to retrieve it is through Pingtable [ref]. Pingtable provides a friendly web interface to retrieve PingER raw data (millions of text files) and load it into a human readable HTML page. However, this is not a web standard and crossing PingER data to generate very specific information may not be possible or extremely difficult using the existing way to retrieve PingER data. This project attempts to provide a standard semantic web format to data retrievable in Pingtable.
 Semantic web [ref] is a W3C recommendation [ref] and is very useful to publish data on the web. The way this project is doing it is storing PingER data in an Open Linked standard format for data interchange on the Web. More specifically, the project is converting PingER data into graph database in Resource Description Framework (RDF) format [ref]. This graph database is very flexible; the schema can be efficiently evolved with time; and the whole data can be retrieved using a Sparql Query [ref].
 The advantages of putting data in this format are that since it is a W3C standard recommendation, there is a great community familiar with it and developing researches on it. Hence, it should be very comfortable to those people to retrieve PingER data. If someone interested in the data never heard about RDF, it is very simple to learn what is needed to use in the project. In addition, we are publishing data in a totally open format so anyone can use it. More importantly, since it is open, someone can come up with a never imagined interesting usage of the data.
 Moreover, our data is being linked to many other existing datasets, such as DBPedia (a graph database for Wikipedia) [ref], Geonames (geographic information), and Freebase [ref]. The data can also be easily linked to any other existing database published in this format. The number of initiatives publishing data in this format has been growing over time so we may also find an even better and totally different usage of this project in a near future [ref]. The graph database is obviously internally linked with its own PingER data so we can create very specific, rich, interesting queries to retrieve data that would never (or extremely difficult to) be retrieved without the project.
 Finally, the existing APIs to handle RDF provide well-known publishing formats such as JSON [ref], CSV [ref], and XML [ref]. We can conveniently get the results of a query and put it into visualization libraries to come up with very interesting visualizations of the data.

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<#comment></#comment>To define the vocabulary used by PingER as well
as its terms, concepts, taxonomy, and relations with each other.<O></O>

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<#comment></#comment>We are basing our ontology on the one proposed
by the project MOMENT[1] described by the article Measurement Ontology for IP traffic (MOI); IP traffic measurement
ontologies architecture, by the European Telecommunications Standards
Institute (ETSI) [2].<O></O>

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<#comment></#comment>More terms and relations were added and edited
on the based ontology. More specifically, metrics were properly added
(Directivity, Conditional Loss Probability, Duplicate Packets, Inter-Packet
Delay Variation, Out of Order Packets, TCP Throughput, Ping Unpredictability,
Ping Unreachability, and Zero Packet Loss Frequency). Statistical measurements
were also added (Inter-quartile Range, Median, 90%, 95%, and 75%). Information
about the nodes were also added based on the Confluence specifications [3]
(Site Name, Nick Name, Full Name, Contact for the host, Comments, Trace Server,
App User, Ping Size, Ping Server, URL, Project Type, and GMT).<O></O>

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<#comment></#comment>Other existing ontologies are also being linked
to our ontology: <O></O>

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<#comment></#comment>Freebase [ref], an open large graph database.<O></O>

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<#comment></#comment>DBPedia [6], the ontology and the resources,
will be used to provide more information about any geographic location or any
other thing Wikipedia has that can be connected to the PingER. So it should be
possible to make very specific queries.<O></O>

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<#comment></#comment>After doing very complex mashups [ref] using the ontology, we need to verify if the ontology
needs any adjustments.<O></O>

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<#comment></#comment>To establish a good environment for the RDF
repository.<O></O>

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<#comment></#comment>We analyzed the existing technologies to make it
possible. There are well-known Triple Storages such as Jena, Sesame, and
Virtuoso [7]. According to [7], Virtuoso
struggles to load large datasets (>1M triples). Hence, we first decided to
try Jena SDB (with MySQL) and then Sesame Native.<O></O>

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i.
<#comment></#comment>Jena SDB results<O></O>

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<#comment></#comment>After establishing Jena SDB 1.3.6 with a native
Java API for storing and querying a relational database MySQL [ref], [ref], we
were able to load a reasonable large amount of data to test. <O></O>

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<#comment></#comment>More specifically, data of all nodes and all
yearly measurement data were loaded. However, querying the dataset was very
disappointing. Some very simple queries, such as listing the value of a given metric
in a given year for a given pair of sites took 7 minutes to run. We only had
less than 100K triples. <O></O>

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<#comment></#comment>Since we would have much more than 100K triples
and 7 minutes is way too long for such a simple query, we decided to change the
RDF repository. In addition, the results of the articles [7] and [8] show that
there are better alternatives. Therefore, Jena SDB may work very well for smaller
datasets, which is not PingER’s case.<O></O>

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ii.
<#comment></#comment>Sesame Native results<O></O>

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<#comment></#comment>Using the results of [7] and [8], we decided to
try Open RDF Sesame Native 2.7.2. This solution requires a Java Webserver as an
interface with the loading program and the RDF repository itself. We are using
the latest Tomcat version, Tomcat 7 [ref]. The physical layer implementation of
the repository provides customization of indexes, which it is said to significantly
enhance the performance of the queries execution [ref].<O></O>

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<#comment></#comment>We are using the indexes spoc,sopc,psoc,posc,opsc,ospc. The order of the indexes for [s]ubject,
[o]bject, and [p]redicate defines the pattern of a search. For example, the
index spoc optimizes a search in
which the subject is the first field of the statement (a statement is composed
by subject, predicate, and object). More specifically, a query that would take
advantage of this index would be: list all instances of metrics (the subject to
be searched for) that are of the “type” “TCP Throughput”. “Type” (the
predicate) and “TCP Throughput” (the object) are given and we are looking for
the metric (the subject). The [c]ontext is a fourth field in “triple”
statements and is used as named graphs in Sesame. Our solution does not
consider contexts, though.<O></O>

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<#comment></#comment>However, multiple indexes increases the used
disk space (to store the indexes) and it also takes longer to load the data to
maintain the indexes.<O></O>

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<#comment></#comment>Note: [7] points out that the largest dataset
known to be loaded into Sesame Native has only 50M triples. If we extrapolate
this number, there is no guarantee that the performance will remain
satisfactory.<O></O>

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<#comment></#comment>Finally, the exactly same query that took 7
minutes to run, in solution – Jena SDB, took less than 5 seconds using solution
(ii) – Open RDF Sesame Native. The circumstances tested were the same in both
environments, i.e., same computer, same dataset, and same query.<O></O>

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<#comment></#comment>Therefore, we decided to migrate the project to
use Open RDF Sesame Native as RDF Repository.<O></O>

Run very complex queries to test
the performance.<O></O>

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<#comment></#comment>Establish an easy way to access the RDF data.<O></O>

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<#comment></#comment>We are using Java Tomcat Webserver to host a Sparql
Endpoint [ref]. The HTML page has a text area in which the user will be able to
write Sparql 1.1 queries [ref] to access the RDF repository. By default, the
results are showed in HTML tables, in a JSP page. However, there will be a
combo box to choose in which format the results will be shown. Available
formats will be triples in CSV, JSON, and XML-RDF.<O></O>

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<#comment></#comment>Use CSS and JavaScript to make it prettier.<O></O>

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<#comment></#comment>Generate RDF data using external datasets and PingER
data.<O></O>

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<#comment></#comment>The process of generating RDF data and
populating the RDF repository is divided into subsections:<O></O>

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i.
<#comment></#comment>Set up the prefixes<O></O>

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<#comment></#comment>Following the RDF standard, all resources are
uniquely identified by an URI [ref]. In order to write less and to provide a
better organization of the statements, it is common to use namespaces
(prefixes) instead of writing absolute URI. For example, it is common to use
the namespace rdfs for the W3C
rdf-schema (<A href="http://www.w3.org/TR/rdf-schema/" mce_href="http://www.w3.org/TR/rdf-schema/">http://www.w3.org/TR/rdf-schema/</A>).<O></O>

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<#comment></#comment>Frequency: If the repository is totally empty,
i.e., it has just been created, the program should first insert the prefixes
into the database. In other words, this should be done only once.<O></O>

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<#comment></#comment>Time to load: Less than 1 second.<O></O>

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ii.
<#comment></#comment>Instantiate continents<O></O>

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<#comment></#comment>The continents are internally instantiated in
the memory of the program so there is no HTTP GET [ref].<O></O>

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<#comment></#comment>Frequency: Only once.<O></O>

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<#comment></#comment>Time to load: Less than 1 second.<O></O>

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iii.
<#comment></#comment>Instantiate countries<O></O>

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<#comment></#comment>The program uses a HTTP GET to access Geonames
API [ref] to retrieve a JSON with data of all countries. Then, for each entry
of the JSON, the program instantiates a country in RDF and loads into the
repository.<O></O>

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<#comment></#comment>Frequency: Only once. 
Time to load: Less than 3 minutes.<O></O>

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text-indent:-98.35pt;mso-text-indent-alt:-18.0pt;mso-list:l6 level1 lfo5;
tab-stops:36.0pt" style="margin-left:98.35pt;mso-add-space:auto;
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tab-stops:36.0pt"><#comment></#comment>
iv.
<#comment></#comment>Generate Node Details JSON<O></O>

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<#comment></#comment>A HTTP GET is done on PingER data to retrieve
the nodes and their information (IP, Nickname, Site Name, Latitude, Longitude, etc.).
A JSON is then generated (and written into a file) to be used by the program.<O></O>

<P class="ListParagraphLevel2" mce_style="margin-left:121.05pt;text-indent:-18.0pt;
mso-list:l3 level1 lfo13;tab-stops:36.0pt" style="margin-left:121.05pt;text-indent:-18.0pt;
mso-list:l3 level1 lfo13;tab-stops:36.0pt"><#comment></#comment>·
<#comment></#comment>Frequency: Same as the generation of the %NODE_DETAILS
(about each 4 hours).<O></O>

<P class="ListParagraphLevel2" mce_style="margin-left:121.05pt;text-indent:-18.0pt;
mso-list:l3 level1 lfo13;tab-stops:36.0pt" style="margin-left:121.05pt;text-indent:-18.0pt;
mso-list:l3 level1 lfo13;tab-stops:36.0pt"><#comment></#comment>·
<#comment></#comment>Time to generate JSON: Less than 4 seconds.<O></O>

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text-indent:-98.35pt;mso-text-indent-alt:-18.0pt;mso-list:l6 level1 lfo5;
tab-stops:36.0pt"><#comment></#comment>
v.
<#comment></#comment>Instantiate Towns and States<O></O>

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<#comment></#comment>For each entry in the Node Details JSON, the
program runs HTTP GETs on the Geonames API to try to find the nearest town
(with at least 1000 habitants) and city (with at least 15000 habitants) based
on the latitude and longitude of the site. The state where the town is located (if
applicable) is also instantiated and linked to the town. The program finally inserts
the instantiated data into the repository.<O></O>

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<#comment></#comment>Note: The program also tries to link the
existing found town with other known RDF datasets (DBPedia [ref] and Freebase
[ref]).<O></O>

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mso-list:l10 level1 lfo12;tab-stops:36.0pt"><#comment></#comment>·
<#comment></#comment>Frequency: Probably
the same as (iv).<O></O>

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mso-list:l10 level1 lfo12;tab-stops:36.0pt" style="margin-left:121.05pt;text-indent:-18.0pt;
mso-list:l10 level1 lfo12;tab-stops:36.0pt"><#comment></#comment>·
<#comment></#comment>Time to load: ~30 minutes.<O></O>

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tab-stops:36.0pt" style="margin-left:98.35pt;mso-add-space:auto;
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tab-stops:36.0pt"><#comment></#comment>
vi.
<#comment></#comment>Instantiate Schools<O></O>

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<#comment></#comment>For each entry in the Node Details JSON, the
program runs HTTP GETs on the DBPedia Sparql Endpoint to try to find a school
whose name is similar to the site’s Full Name, on Node Details JSON.<O></O>

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<#comment></#comment>If found, an instance of a school is inserted
into the repository. Information about school includes endowment, number of
students (undergrad and postgrad), faculty size, etc.) <O></O>

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<#comment></#comment>Note: The program also tries to link the
existing found school with Freebase.<O></O>

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<#comment></#comment>Note2: DBPedia (just like Wikipedia) is not
complete. Thus, for example, information that is available for a very famous
and well-known university may not be available for another university that is
not as famous.<O></O>

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mso-list:l10 level1 lfo12;tab-stops:36.0pt"><#comment></#comment>·
<#comment></#comment>Frequency: Probably
the same as (iv).<O></O>

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mso-list:l10 level1 lfo12;tab-stops:36.0pt" style="margin-left:121.05pt;text-indent:-18.0pt;
mso-list:l10 level1 lfo12;tab-stops:36.0pt"><#comment></#comment>·
<#comment></#comment>Time to load: ~30 minutes.<O></O>

<P class="ListSubSubtitle" mce_style="margin-left:98.35pt;mso-add-space:auto;
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tab-stops:36.0pt" style="margin-left:98.35pt;mso-add-space:auto;
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tab-stops:36.0pt"><#comment></#comment>
vii.
<#comment></#comment>Instantiate Nodes<O></O>

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<#comment></#comment>After instantiating Towns, States, Schools,
Continents, and Countries, we can finally instantiate the PingER nodes.<O></O>

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<#comment></#comment>Again, for each entry of the Node Details JSON,
the program instantiates a Node linking it with its respective Town, School (if
it is one), Country, State (if applicable), and Continent.<O></O>

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mso-list:l8 level1 lfo16;tab-stops:36.0pt"><#comment></#comment>·
<#comment></#comment>Frequency: Probably
the same as (iv).<O></O>

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mso-list:l8 level1 lfo16;tab-stops:36.0pt" style="margin-left:121.05pt;text-indent:-18.0pt;
mso-list:l8 level1 lfo16;tab-stops:36.0pt"><#comment></#comment>·
<#comment></#comment>Time to load: Less than 3 minutes.<O></O>

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<#comment></#comment>Make better (more precise and informative)
annotations of how long each step above takes to run. Maybe space utilized
should also be included.<O></O>

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<#comment></#comment>Complex tests should be performed.<O></O>

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<#comment></#comment>Study other forms of retrieving information about
schools.<O></O>

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<#comment></#comment>Study ways to optimize this process. Maybe
parallelizing? <O></O>

<P class="ListTitle" mce_style="margin-left:1.0cm;mso-add-space:auto;text-indent:
0cm;mso-list:l1 level1 lfo1;tab-stops:36.0pt" style="margin-left:1.0cm;mso-add-space:auto;text-indent:
0cm;mso-list:l1 level1 lfo1;tab-stops:36.0pt"><#comment></#comment>6. <#comment></#comment>Loading the RDF Repository
(PingER Measurements)<O></O>

<P class="ListParagraphLevel1" mce_style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt" style="text-indent:-2.0cm;mso-text-indent-alt:
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<#comment></#comment>Although this step remains in the same line of
the previous one (4), it can be separated into a totally different context –
the measurements context, just as a matter of better organization and
understanding. The goal is to generate RDF data from PingER dataset and load it
into the repository.<O></O>

<P class="ListSubtitle" mce_style="margin-left:1.0cm;mso-add-space:auto;text-indent:
12.75pt;mso-list:l1 level2 lfo1;tab-stops:36.0pt" style="margin-left:1.0cm;mso-add-space:auto;text-indent:
12.75pt;mso-list:l1 level2 lfo1;tab-stops:36.0pt"><#comment></#comment>b. <#comment></#comment>Progress<O></O>

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<#comment></#comment>After loading the repository with the data
specified in the previous step (4), the program needs to load PingER measurement
data.<O></O>

<P class="ListParagraphLevel1" mce_style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt" style="text-indent:-2.0cm;mso-text-indent-alt:
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<#comment></#comment>The first step in this process is to generate
the Monitoring-Monitored [ref] JSON. The program executes a HTTP GET in <A href="http://www-wanmon.slac.stanford.edu/cgi-wrap/dbprac.pl?monalias=all" mce_href="http://www-wanmon.slac.stanford.edu/cgi-wrap/dbprac.pl?monalias=all">http://www-wanmon.slac.stanford.edu/cgi-wrap/dbprac.pl?monalias=all</A>
to retrieve all monitoring nodes. Then, for each monitoring node, another HTTP
GET is executed in <A href="http://www-wanmon.slac.stanford.edu/cgi-wrap/dbprac.pl?monalias=MONITORING_NODE&find=1" mce_href="http://www-wanmon.slac.stanford.edu/cgi-wrap/dbprac.pl?monalias=MONITORING_NODE&find=1">http://www-wanmon.slac.stanford.edu/cgi-wrap/dbprac.pl?monalias= EDU.SLAC.STANFORD.N3&find=1</A>,
where the value of monalias is a
given monitoring node, to retrieve the monitored nodes by that monitoring node.
The Monitoring-Monitored JSON is then generated and written into a file.<O></O>

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mso-list:l2 level1 lfo21;tab-stops:36.0pt"><#comment></#comment>·
<#comment></#comment>Frequency of generating this JSON: Probably the same as (4.iv)<O></O>

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mso-list:l2 level1 lfo21;tab-stops:36.0pt" style="margin-left:92.7pt;text-indent:-18.0pt;
mso-list:l2 level1 lfo21;tab-stops:36.0pt"><#comment></#comment>·
<#comment></#comment>Time to generate: Less than 2 minutes.<O></O>

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<#comment></#comment>Having the JSON, the instantiating process
happens according to this approach: For each monitoring node (entry of the
JSON), for each metric, for each packet size, for each time parameter, the
program executes a HTTP GET in the Pingtable [ref] Tab Separated Values (TSV)
file specified by crossing all these parameters. A TSV URL example is of the
form <O></O>

<P class="ListParagraphLevel1" mce_style="text-indent:-2.0cm;mso-text-indent-alt:
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<#comment></#comment><A href="http://www-wanmon.slac.stanford.edu/cgi-wrap/pingtable.pl?format=tsv&file=average_rtt&by=by-node&size=100&tick=allyearly&from=EDU.SLAC.STANFORD.N3&to=WORLD&ex=none&only=all&dataset=hep&percentage=any" mce_href="http://www-wanmon.slac.stanford.edu/cgi-wrap/pingtable.pl?format=tsv&file=average_rtt&by=by-node&size=100&tick=allyearly&from=EDU.SLAC.STANFORD.N3&to=WORLD&ex=none&only=all&dataset=hep&percentage=any">http://www-wanmon.slac.stanford.edu/cgi-wrap/pingtable.pl?format=tsv&file=average_rtt&by=by-node&size=100&tick=allyearly&from=EDU.SLAC.STANFORD.N3&to=WORLD&ex=none&only=all&dataset=hep&percentage=any</A><O></O>

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<#comment></#comment>Where the parameters:<O></O>

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mso-list:l4 level3 lfo18;tab-stops:36.0pt"><#comment></#comment>·
<#comment></#comment>from – is the monitoring node that pings other
monitored nodes.<O></O>

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mso-list:l4 level3 lfo18;tab-stops:36.0pt" style="margin-left:59.55pt;text-indent:-2.85pt;
mso-list:l4 level3 lfo18;tab-stops:36.0pt"><#comment></#comment>·
<#comment></#comment>tick – represents the time aggregation. PingER
has data from 1998 to 2013. At this moment, the project is considering only the
following tick parameters:<O></O>

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0cm;margin-left:88.8pt;margin-bottom:.0001pt;text-indent:-18.0pt;mso-list:l7 level1 lfo20;
tab-stops:36.0pt" style="margin-top:0cm;margin-right:0cm;margin-bottom:
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tab-stops:36.0pt"><#comment></#comment>-
<#comment></#comment>allyearly<O></O>

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0cm;margin-left:88.8pt;margin-bottom:.0001pt;text-indent:-18.0pt;mso-list:l7 level1 lfo20;
tab-stops:36.0pt" style="margin-top:0cm;margin-right:0cm;margin-bottom:
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tab-stops:36.0pt"><#comment></#comment>-
<#comment></#comment>allmonthly<O></O>

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mso-list:l4 level3 lfo18;tab-stops:36.0pt"><#comment></#comment>·
<#comment></#comment>size – is the packet size. At this moment, the
project is considering only packets of size 100 bytes.<O></O>

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mso-list:l4 level3 lfo18;tab-stops:36.0pt" style="margin-left:59.55pt;text-indent:-2.85pt;
mso-list:l4 level3 lfo18;tab-stops:36.0pt"><#comment></#comment>·
<#comment></#comment>file – is the network measurement metric. At
this moment, the project is considering only the following metrics:<O></O>

<P class="ListParagraphLevel1" mce_style="margin-top:0cm;margin-right:0cm;margin-bottom:
0cm;margin-left:92.7pt;margin-bottom:.0001pt;text-indent:-18.0pt;mso-list:l11 level1 lfo19;
tab-stops:36.0pt" style="margin-top:0cm;margin-right:0cm;margin-bottom:
0cm;margin-left:92.7pt;margin-bottom:.0001pt;text-indent:-18.0pt;mso-list:l11 level1 lfo19;
tab-stops:36.0pt"><#comment></#comment>-
<#comment></#comment>Mean Opinion Scores<O></O>

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0cm;margin-left:92.7pt;margin-bottom:.0001pt;text-indent:-18.0pt;mso-list:l11 level1 lfo19;
tab-stops:36.0pt" style="margin-top:0cm;margin-right:0cm;margin-bottom:
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tab-stops:36.0pt"><#comment></#comment>-
<#comment></#comment>Directivity<O></O>

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0cm;margin-left:92.7pt;margin-bottom:.0001pt;text-indent:-18.0pt;mso-list:l11 level1 lfo19;
tab-stops:36.0pt" style="margin-top:0cm;margin-right:0cm;margin-bottom:
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tab-stops:36.0pt"><#comment></#comment>-
<#comment></#comment>Average Round Trip Time<O></O>

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0cm;margin-left:92.7pt;margin-bottom:.0001pt;text-indent:-18.0pt;mso-list:l11 level1 lfo19;
tab-stops:36.0pt" style="margin-top:0cm;margin-right:0cm;margin-bottom:
0cm;margin-left:92.7pt;margin-bottom:.0001pt;text-indent:-18.0pt;mso-list:l11 level1 lfo19;
tab-stops:36.0pt"><#comment></#comment>-
<#comment></#comment>Conditional Loss Probability<O></O>

<P class="ListParagraphLevel1" mce_style="margin-top:0cm;margin-right:0cm;margin-bottom:
0cm;margin-left:92.7pt;margin-bottom:.0001pt;text-indent:-18.0pt;mso-list:l11 level1 lfo19;
tab-stops:36.0pt" style="margin-top:0cm;margin-right:0cm;margin-bottom:
0cm;margin-left:92.7pt;margin-bottom:.0001pt;text-indent:-18.0pt;mso-list:l11 level1 lfo19;
tab-stops:36.0pt"><#comment></#comment>-
<#comment></#comment>Duplicate Packets<O></O>

<P class="ListParagraphLevel1" mce_style="margin-top:0cm;margin-right:0cm;margin-bottom:
0cm;margin-left:92.7pt;margin-bottom:.0001pt;text-indent:-18.0pt;mso-list:l11 level1 lfo19;
tab-stops:36.0pt" style="margin-top:0cm;margin-right:0cm;margin-bottom:
0cm;margin-left:92.7pt;margin-bottom:.0001pt;text-indent:-18.0pt;mso-list:l11 level1 lfo19;
tab-stops:36.0pt"><#comment></#comment>-
<#comment></#comment>Inter Packet Delay Variation<O></O>

<P class="ListParagraphLevel1" mce_style="margin-top:0cm;margin-right:0cm;margin-bottom:
0cm;margin-left:92.7pt;margin-bottom:.0001pt;text-indent:-18.0pt;mso-list:l11 level1 lfo19;
tab-stops:36.0pt" style="margin-top:0cm;margin-right:0cm;margin-bottom:
0cm;margin-left:92.7pt;margin-bottom:.0001pt;text-indent:-18.0pt;mso-list:l11 level1 lfo19;
tab-stops:36.0pt"><#comment></#comment>-
<#comment></#comment>Minimum Round Trip Delay<O></O>

<P class="ListParagraphLevel1" mce_style="margin-top:0cm;margin-right:0cm;margin-bottom:
0cm;margin-left:92.7pt;margin-bottom:.0001pt;text-indent:-18.0pt;mso-list:l11 level1 lfo19;
tab-stops:36.0pt" style="margin-top:0cm;margin-right:0cm;margin-bottom:
0cm;margin-left:92.7pt;margin-bottom:.0001pt;text-indent:-18.0pt;mso-list:l11 level1 lfo19;
tab-stops:36.0pt"><#comment></#comment>-
<#comment></#comment>Packet Loss<O></O>

<P class="ListParagraphLevel1" mce_style="margin-top:0cm;margin-right:0cm;margin-bottom:
0cm;margin-left:92.7pt;margin-bottom:.0001pt;text-indent:-18.0pt;mso-list:l11 level1 lfo19;
tab-stops:36.0pt" style="margin-top:0cm;margin-right:0cm;margin-bottom:
0cm;margin-left:92.7pt;margin-bottom:.0001pt;text-indent:-18.0pt;mso-list:l11 level1 lfo19;
tab-stops:36.0pt"><#comment></#comment>-
<#comment></#comment>TCP Throughput<O></O>

<P class="ListParagraphLevel1" mce_style="margin-top:0cm;margin-right:0cm;margin-bottom:
0cm;margin-left:92.7pt;margin-bottom:.0001pt;text-indent:-18.0pt;mso-list:l11 level1 lfo19;
tab-stops:36.0pt" style="margin-top:0cm;margin-right:0cm;margin-bottom:
0cm;margin-left:92.7pt;margin-bottom:.0001pt;text-indent:-18.0pt;mso-list:l11 level1 lfo19;
tab-stops:36.0pt"><#comment></#comment>-
<#comment></#comment>Unreachability<O></O>

<P class="ListParagraphLevel1" mce_style="margin-top:0cm;margin-right:0cm;margin-bottom:
0cm;margin-left:92.7pt;margin-bottom:.0001pt;text-indent:-18.0pt;mso-list:l11 level1 lfo19;
tab-stops:36.0pt" style="margin-top:0cm;margin-right:0cm;margin-bottom:
0cm;margin-left:92.7pt;margin-bottom:.0001pt;text-indent:-18.0pt;mso-list:l11 level1 lfo19;
tab-stops:36.0pt"><#comment></#comment>-
<#comment></#comment>Zero Packet Loss Frequency<O></O>

<P class="ListParagraphLevel1" mce_style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt" style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt"><#comment></#comment>
<#comment></#comment>Note: This process is totally independent of the
previous step (4). Hence it can be independently parallelized. However, if this
step is executed before the previous, the measurement information regarding the
nodes will point to broken links, which is not a big problem and does not
prevent loading measurement data. The broken links will be automatically
repaired when the nodes are successfully instantiated (section 4.vii). <O></O>

<P class="ListSubtitle" mce_style="margin-left:1.0cm;mso-add-space:auto;text-indent:
12.75pt;mso-list:l1 level2 lfo1;tab-stops:36.0pt" style="margin-left:1.0cm;mso-add-space:auto;text-indent:
12.75pt;mso-list:l1 level2 lfo1;tab-stops:36.0pt"><#comment></#comment>c. <#comment></#comment>Performance Evaluation<O></O>

<P class="ListParagraphLevel1" mce_style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt" style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt"><#comment></#comment><SPAN mce_style="mso-fareast-font-family:Calibri;mso-fareast-theme-font:minor-latin;
mso-bidi-font-family:Calibri;mso-bidi-theme-font:minor-latin;color:red" style="mso-fareast-font-family:Calibri;mso-fareast-theme-font:minor-latin;
mso-bidi-font-family:Calibri;mso-bidi-theme-font:minor-latin;color:red">
<#comment></#comment>Last 365 days: For each monitoring node, for
each metric, it is taking around 1 hour to load the data into the repository.
Hence, for 80 monitoring, for the 11 metrics, it is going to take approximately
880 hours (36 days). Thus, impracticable amount of time.<O></O>

<P class="ListParagraphLevel1" mce_style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt" style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt"><#comment></#comment>
<#comment></#comment>It is taking a huge amount of time to load the entire
data. We must optimize this process.<O></O>

<P class="ListParagraphLevel1" mce_style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt" style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt"><#comment></#comment>
<#comment></#comment>Measure time and space taken to load the
repository.<O></O>

<P class="ListParagraphLevel1" mce_style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt" style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt"><#comment></#comment>
<#comment></#comment>Complex tests.<O></O>

<P class="ListTitle" mce_style="margin-left:1.0cm;mso-add-space:auto;text-indent:
0cm;mso-list:l1 level1 lfo1;tab-stops:36.0pt" style="margin-left:1.0cm;mso-add-space:auto;text-indent:
0cm;mso-list:l1 level1 lfo1;tab-stops:36.0pt"><#comment></#comment>7. <#comment></#comment>Rich visualization of the data<O></O>

<P class="ListParagraphLevel1" mce_style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt" style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt"><#comment></#comment>
<#comment></#comment>Provide smart and useful visualization of PingER
data in RDF format.<O></O>

<P class="ListSubtitle" mce_style="margin-left:1.0cm;mso-add-space:auto;text-indent:
12.75pt;mso-list:l1 level2 lfo1;tab-stops:36.0pt" style="margin-left:1.0cm;mso-add-space:auto;text-indent:
12.75pt;mso-list:l1 level2 lfo1;tab-stops:36.0pt"><#comment></#comment>b. <#comment></#comment>Progress<O></O>

<P class="ListParagraphLevel1" mce_style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt" style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt"><#comment></#comment>
<#comment></#comment>We studied to possibility of using 3 APIs:<O></O>

<P class="ListSubSubtitleCxSpFirst" mce_style="margin-left:98.35pt;mso-add-space:
auto;text-indent:-98.35pt;mso-text-indent-alt:-18.0pt;mso-list:l6 level1 lfo11;
tab-stops:36.0pt" style="margin-left:98.35pt;mso-add-space:
auto;text-indent:-98.35pt;mso-text-indent-alt:-18.0pt;mso-list:l6 level1 lfo11;
tab-stops:36.0pt"><#comment></#comment>
i.
<#comment></#comment>Google Maps JavaScript API v3 [ref]<O></O>

<P class="ListSubSubtitleCxSpMiddle" mce_style="margin-left:98.35pt;mso-add-space:
auto;text-indent:-98.35pt;mso-text-indent-alt:-18.0pt;mso-list:l6 level1 lfo5;
tab-stops:36.0pt" style="margin-left:98.35pt;mso-add-space:
auto;text-indent:-98.35pt;mso-text-indent-alt:-18.0pt;mso-list:l6 level1 lfo5;
tab-stops:36.0pt"><#comment></#comment>
ii.
<#comment></#comment>Google Geo Charts [ref]<O></O>

<P class="ListSubSubtitleCxSpLast" mce_style="margin-left:98.35pt;mso-add-space:auto;
text-indent:-98.35pt;mso-text-indent-alt:-18.0pt;mso-list:l6 level1 lfo5;
tab-stops:36.0pt" style="margin-left:98.35pt;mso-add-space:auto;
text-indent:-98.35pt;mso-text-indent-alt:-18.0pt;mso-list:l6 level1 lfo5;
tab-stops:36.0pt"><#comment></#comment>
iii.
<#comment></#comment>Google Public Data Explorer [ref]<O></O>

<P class="ListParagraphLevel1" mce_style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt" style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt"><#comment></#comment>
<#comment></#comment>All of them seem to be very useful and can
provide rich visualizations. and (iii) seem to be the most powerful of
them.<O></O>

<P class="ListParagraphLevel1" mce_style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt" style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt"><#comment></#comment>
<#comment></#comment>We need to think about good and useful mashups to
use within this entire project and showing them on these visualization APIs.
One type of mashup that is being investigated is retrieving data from DBPedia
to cross PingER data with information related to universities (such as
endowment, number of students, if the university is public or private, etc).<O></O>

<P class="ListTitle" mce_style="margin-left:1.0cm;mso-add-space:auto;text-indent:
0cm;mso-list:l1 level1 lfo1;tab-stops:36.0pt" style="margin-left:1.0cm;mso-add-space:auto;text-indent:
0cm;mso-list:l1 level1 lfo1;tab-stops:36.0pt"><#comment></#comment>8. <#comment></#comment>Documentation<O></O>

<P class="ListParagraphLevel1" mce_style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt" style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt"><#comment></#comment>
<#comment></#comment>Document the whole project.<O></O>

<P class="ListSubtitle" mce_style="margin-left:1.0cm;mso-add-space:auto;text-indent:
12.75pt;mso-list:l1 level2 lfo1;tab-stops:36.0pt" style="margin-left:1.0cm;mso-add-space:auto;text-indent:
12.75pt;mso-list:l1 level2 lfo1;tab-stops:36.0pt"><#comment></#comment>b. <#comment></#comment>Progress<O></O>

<P class="ListParagraphLevel1" mce_style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt" style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt"><#comment></#comment>
<#comment></#comment>This Project Progress is being built. It is
being kept in both MS WORD format and HTML (to be used in Confluence).<O></O>

<P class="ListParagraphLevel1" mce_style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt" style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt"><#comment></#comment>
<#comment></#comment>The Confluence page To do-Doing-Done is kept updated more frequently.<O></O>

<P class="ListParagraphLevel1" mce_style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt" style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt"><#comment></#comment>
<#comment></#comment>The Java Project should be documented. Javadocs
are supposed to be generated for each class and method.<O></O>

<P class="ListParagraphLevel1" mce_style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt" style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt"><#comment></#comment>
<#comment></#comment>An interactive JavaScript document is to be
generated to graphically represent the ontology in order to support users to
use the RDF data.<O></O>

<P class="ListParagraphLevel1" mce_style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt" style="text-indent:-2.0cm;mso-text-indent-alt:
0cm;mso-list:l1 level3 lfo1;tab-stops:36.0pt"><#comment></#comment>
<#comment></#comment>Installation guide should be written. This
should include how to configure the environment and everything needed to
compile and run the project. Both the RDF Repository (with Tomcat settings) and
the Sparql Endpoint projects should have an installation guide.<O></O>

<P class="ListTitle" mce_style="margin-left:1.0cm;mso-add-space:auto;text-indent:
0cm;mso-list:l1 level1 lfo1;tab-stops:36.0pt" style="margin-left:1.0cm;mso-add-space:auto;text-indent:
0cm;mso-list:l1 level1 lfo1;tab-stops:36.0pt"><#comment></#comment>9. <#comment></#comment>References<O></O>

[1] Project MOMENT Ontologies. Retrieved from <A href="https://svn.fp7-moment.eu/svn/moment/public/Ontology/" mce_href="https://svn.fp7-moment.eu/svn/moment/public/Ontology/">https://svn.fp7-moment.eu/svn/moment/public/Ontology/</A>
on June 5, 2013.<O></O>

[2] ETSI. Measurement Ontology for IP traffic (MOI); IP
traffic measurement ontologies architecture. Retrieved from <A href="http://www.etsi.org/deliver/etsi_gs/MOI/001_099/003/01.01.01_60/gs_moi003v010101p.pdf" mce_href="http://www.etsi.org/deliver/etsi_gs/MOI/001_099/003/01.01.01_60/gs_moi003v010101p.pdf">http://www.etsi.org/deliver/etsi_gs/MOI/001_099/003/01.01.01_60/gs_moi003v010101p.pdf</A>
on June 5, 2013.<O></O>

[3] PingER Node Details. Retrieved from <A href="https://confluence.slac.stanford.edu/display/IEPM/PingER+NODEDETAILS" mce_href="../../../../../../../../../display/IEPM/PingER+NODEDETAILS">https://confluence.slac.stanford.edu/display/IEPM/PingER+NODEDETAILS</A>
on June 5, 2013.<O></O>

[4] Food And Agriculture Organization Of The United Nations
Ontology. Retrieved from <A href="http://www.fao.org/countryprofiles/geoinfo/geopolitical/" mce_href="http://www.fao.org/countryprofiles/geoinfo/geopolitical/">http://www.fao.org/countryprofiles/geoinfo/geopolitical/</A>
on June 5, 2013.<O></O>

[5] Friend of a Friend Ontology. Retrieved from <A href="http://www.foaf-project.org/" mce_href="http://www.foaf-project.org/">http://www.foaf-project.org/</A> on June 5,
2013.<O></O>

[6] DBPedia. Retrieved from <A href="http://dbpedia.org/About" mce_href="http://dbpedia.org/About">http://dbpedia.org/About</A> on June 5, 2013.<O></O>

[7] Comparison of Triple Stores. Retrieved from <A href="http://www.bioontology.org/wiki/images/6/6a/Triple_Stores.pdf" mce_href="http://www.bioontology.org/wiki/images/6/6a/Triple_Stores.pdf">http://www.bioontology.org/wiki/images/6/6a/Triple_Stores.pdf</A>
on June 5, 2013.<O></O>

Child pages

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