By Paul R. Cohen, Niall M. Adams, Michael R. Berthold
This booklet constitutes the refereed court cases of the ninth foreign convention on clever info research, IDA 2010, held in Tucson, AZ, united states in might 2010. The 21 revised papers provided including 2 invited papers have been conscientiously reviewed and chosen from greater than forty submissions. All present features of clever facts research are addressed, relatively clever aid for modeling and interpreting advanced, dynamical structures. issues lined are end-to-end software program structures; modeling advanced structures comparable to gene regulatory networks, financial structures, ecological structures, assets similar to water, and dynamical social platforms similar to on-line groups; and robustness, scaling houses and different usability concerns.
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Additional resources for Advances in Intelligent Data Analysis IX: 9th International Symposium, IDA 2010, Tucson, AZ, USA, May 19-21, 2010, Proceedings (Lecture Notes in ... Applications, incl. Internet Web, and HCI)
Each word is replaced by its lemma and linguistic informations. That step deﬁnes the items of the sequence database. The POS tagged text is then sliced in sequences (Step 2). The type of slice size (a sequence) can be for example the phrase, the whole sentence or the paragraph. Sequential pattern mining is then applied (Step 3) to ﬁnd the frequent sequential patterns in the database. The patterns are then ﬁltered with respect to user-deﬁned constraints (Step 4). Method CheckConstrainsts prunes the sequential patterns that do not satisfy CG .
To further investigate the possibility of detecting earthquakes based on citizen reports, we compared earthquake related tweet activity against the USGS earthquake catalog. To do this comparison we created a time series of tweets-per-minute using a month and a half of archived keyword filtered tweet data as shown in Fig. 4. We then searched the time series for sudden increases in temporally related tweets and then correlated these peaks with earthquakes. All of the major spikes, with the exception of one on October 15th, coincide with earthquakes.
Without quantitative verified data, alerts provoking response measures are not possible. The main advantage of Twitter is speed, especially in sparsely instrumented areas. Not all tweets containing the word earthquake or quake, in any language, correspond to people feeling shaking caused by an earthquake. Analysis of data for the past few months indicates that the background level of noise (out of context tweets geographically and time clustered) is generally very low, except following major earthquakes.