Valoración de la pertinencia y calidad del modelo inicial

The scoring function (tagged with number 3) constitutes the second half of the

Organisational Policy as shown in Figure 5.1. In order to evaluate the veracity of a message, the scoring function essentially uses the information provenance and other quality factors of interest (for a given context or incident) and specifies how to assign scores to messages for each factor. The selection of factors will depend upon context, since relevance and reliability of the factors as a trust indicator may vary according to their vulnerability to compromise, the likelihood of a compromise (malicious or accidental) taking place, as well as the nature of the incident. For example, in the case of a natural disaster (e.g. landslide, earthquake, etc.),Location of Informer (i.e. location of the source of information) is an important factor for assessing the correctness and reliability of the given information. This is due to the fact that people located far from the incident location may not know the information very well and even if they do know, they are unlikely to know the information at first hand.

A history of reputation can be maintained for each of the informants who pro- vided information, which may be useful in future events/incidents. An informant may receive a positive score (e.g. +1) for each piece of information, s/he provided, which was correct and a negative score (e.g. -1) for each piece of information, which was incorrect. However, since reputation of an agent builds over time and it (reputa- tion) is measured from past experience/interaction with the same agent,Reputation

may not be an appropriate factor for assessing the correctness and reliability of in- formation in this type of incidents, unless people in a particular region experienced similar incidents several times before.

Since, it is likely that the freshest (the latest generated) piece of information pro- vides some previously unknown/undiscovered information, freshness of information can play an important role in prioritising information, especially when some pieces of information are contradictory. A time-stamp indicates the time when a piece of information was generated or released. Thus the time-stamp helps to determine the

Freshness and timeliness of a piece of information. Freshness of information can be used in two steps.

Firstly, it allows determining whether a piece of information is timely or stale i.e. whether a piece of information refers to an incident that took place in the past and we have closed that case and are not collecting information about that incident any more.

Secondly, if a piece of information is not stale, then we need to find out whether it is contradictory to any existing information or not. In principle, if a piece of information is supplementary or complementary to the existing information, then it may receive the highest score, (say) 10 and the score of the previous messages will remain unchanged. But if it is contradictory, then the latest information will receive the highest score, (say) 10 and the score of older pieces of information will reduce according to the Organisational Policy. For example, the scores of older pieces of information may be reduced by 1 for every additional one or two hours of their age, counting from the current processing time (or from the time when the latest but contradictory information was generated).

According to the policy, the scoring function will assign a vector of scores to each piece of information, one score for each of the provenance factors in use. In other words, the overall score given to a piece of information is a vector constituted of multiple scores; each score assigned against each of its provenance factors. Here is an example of two vector scores (a and b) that may be assigned to two different messages:

a = (l, f, c) = (7, 6, 7)

b = (i, l, f, r, c) = (3, 7, 8, 5, 6)

where, i ≡ Identity, l ≡ Location, f ≡ Freshness, r ≡ Reputation, and c ≡ Cor- roboration

The Organisational Policy will also specify how large or small a score should be, when a score will be assigned to each factor in a particular situation. For example, in an incident where eye-witness accounts are of paramount interest, then the policy may heavily penalise the locations that are not in the immediate area of interest. Likewise, where identity is of paramount interest and a white list and a black list is maintained for the information sources, then the sources on the white list may be given higher scores than those that are not on that list and the sources on the black list may be heavily penalised, if considered at all. The scored messages will be stored in a database for consistency analysis and conflict resolution.

5.1.3.1 Exemplification of Scoring Function

I have given an example of how a message can be scored according to a simple and contrived policy, which uses three of the trust factors, Location, Freshness and Reputation. How the location-score can vary according to the proximity of the informer to the incident location is defined in Table 5.1 and how the score varies depending on the age of information is set out in Table 5.2.

Table 5.1: Scoring Policy for Location

Proximity (Mile) Score

0.5 or less 10 1 or less 9 2 or less 8 3 or less 7 4 or less 6 5 or less 5 7 or less 4 9 or less 3 10 or less 2 10 or more 1 Note: Proximity indicates the distance between the Location of Incident and the Location of Informant.

Table 5.2: Scoring Policy for Freshness or Timeliness

Age of Information (Hour) Score

2 or less 10 4 or less 9 6 or less 8 8 or less 7 10 or less 6 14 or less 5 18 or less 4 24 or less 3 36 or less 2 48 or less 1 Note: The age of information is calcu- lated from the current processing time i.e. it refers to the time elapsed since in- formation was generated/released. The time when the information was generat- ed/released is acquired from the time- stamp generated by a third party e.g. Twitter, Facebook, and Mobile Net- works.

Suppose an incident has taken place in Place-X on 30/06/2013 at 13:20 and two messages have been received that are reporting about the incident.

• Message-1 was sent by Person-A from Place-Y on 30/06/2013 at 13.25.

• Message-2 was sent by Person-B from Place-Z on 30/06/2013 at 16:00.

• Distance between Place-X and Place-Y is 0.5 mile.

(i.e. the distance between the incident-location and the location of Person-A, when s/he sent the message, is 0.5 mile. )

Table 5.3: Scoring Policy for Reputation

InformerID Score Note

Person-B 6 Person-B provided six pieces of information in the past and all of them were correct.

[email protected] 4 [email protected] provided four correct pieces of information in the past.

[email protected] -2 [email protected] provided four pieces of informa- tion in the past but three of them were wrong and only one was correct. Hence, -3 + 1 = -2

• Distance between Place-X and Place-Z is 4 miles.

(i.e. the distance between the incident-location and the location of Person-B, when s/he sent the message, is 4 miles.)

• Person-A does not have a reputation history i.e. the score is 0.

• Person-B has a reputation score 6 (i.e. Person-B correctly provided six pieces of information in the past).

Based on the information listed above and the scoring policy set out in Table 5.1 and Table 5.2, the vector score (l, f, r) received by Message-1 and Message-2 will be (10, 10, 0) and (6, 9, 6) respectively.

How the overall total score of a World View can be calculated by combining individual vector scores of each message is discussed in Section 5.1.5.