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cannot be guaranteed that the labels have been correctly assigned. On the other hand, if the labels are different, they can be merged by syntactically intersecting the dimension headers with a high threshold. 4) Default Occasionally, the methods outlined in 1, 2, and 3 may fail to identify an appropriate label for the dimensions. Nevertheless, there may be a remaining list from the candidate list in heuristic 2 that has not been assigned as a label to any of the dimensions. The candidates derived from the dimension summary headers have the highest priority to be assigned as a label, because of the nature of this location in regards to the dimension. If a dimension summary header was not found, then the non-assigned candidate from the table title is assigned as the label without any processing. V. IDENTIFYING isA RELATIONSHIPS FROM A CANDIDATE SET In this section, the candidate set is constructed using the table title and the dimension summary header. One of the candidates in this set can be the dimension label. This paper introduces a special algorithm to reach this goal. The algorithm is run in three steps. In short, there should be a candidate set, i.e., { , and the dimension headers; i.e., { ,, }. The problem is to select a , 1
The table title and the dimension summary header are most unlikely full sentences. Many resemble noun phrases. Therefore, they need to be partitioned to appropriate sizes in order to find a label to be matched with the ancestor set. Those labels could be phrases or words. This study has developed three different ways of partitioning in order to find the best method to choose the candidate set. The candidates can be partitioned into NLP chunks, or they can be partitioned to the WordNet synset and the full chunk that would serve as a label can then be retrieved. The third method is a combination of the previous two. 1) Case #1: NLP chunks Chunking any sentence/phrase, in this case the table title and the dimension summary header, depends on the part of speech tagging of words in the sentence. These chunks are considered as candidates for the label that covers the dimension headers. We refer to “Statistical parsing of English sentences” [17] to divide the candidates into appropriate noun phrases, verb phrases and words. For example, for the table title in Fig. 2, the chunks are {Cancer, new cases, selected primary site of cancer, sex}, which become candidates in the candidate set. 2) Case #2: single words Each candidate in the candidate set will be a single WordNet synset that is found in the table title and the dimension summary header. 3) Case #3: sliding window chunks A sliding window technique is formed on the table title and the dimension summary header. The candidates in the candidate set are of size 1, 2 or 3 WordNet synsets picked from this technique. C. Assigning Label from the Candidate Set This step computes the semantic similarity between the list of ancestors, derived in step A, and the candidates in the candidate set, derived in step B. This is done using the semantic score presented in “WordNet-based semantic similarity measurement” [18]. The semantic similarities are computed between the ancestors in the ancestor set and the candidate set, beginning with the highest scoring ancestor and the candidates that appear after the prepositions in the table title. The candidate that matches one of the ancestors is selected as a label. This procedure is done in two steps, depending on the type of candidates in the candidate set. The steps are presented in the following algorithm. 1: for each in { , ..., } 2: for each candidate in { 3: divide each to each WordNet synset { 4: for each 5: identify LCA between and , the depth d for and , 6: Sim= LCA / ) 7: end 8: end 9: if Case #1 or Case#3 then{ compute the similarity for the ancestor and the full chunk 10: = 11: = 12: T= ( + )/ ( + ) 13: if T certain threshold then{ 14: L = 15: quit loop} 16: else if Case#2 then{ 17: if (Max Sim certain threshold) then{ 18: L = 19: quit loop} When the candidates are single WordNet synsets, the semantic similarity between each ancestor and each candidate is measured according to Wu and Palmer [19], as shown in (2), where LCA is the least common ancestor depth in WordNet taxonomy between two WordNet synsets; i.e., the ancestor and the candidate, is the depth for the ancestor, and is the depth for the candidate word. Sim= LCA / ) (2) When the candidates are chunks, the chunks must be divided into single WordNet synset and their semantic similarity computed with the ancestors. This is followed with a computing of the full similarity of the full chunk, since any semantic similarity function cannot compute the similarity directly between the phrase and words, because the chunk contains more than one WordNet synset and the ancestor is one WordNet synset. Therefore, it is necessary to ensure that the semantic similarity measure considers comparisons of a group of WordNet synsets taken together. Since (2) computes the similarity between two WordNet synsets, the similarity scores for the case when the candidate is a phrase must be collected. According to [18] the total similarity, T, is equal to the summation of the similarities between the phrase and the ancestor, p1, and p2 is the highest similarity score between the ancestor and a word in the phrase. This is divided by the summation of their length l1 and l2. T= (p1+p2)/ (l1+l2) (3) VI. EXPERIMENTAL RESULTS Our system was tested with multidimensional tables from Statistics Canada’s website of summary tables [20] and from Statistics Austria website [21]. The tables are freely accessible and belong to a few national statistical agencies that continue to publish in HTML. They contain invaluable quantities of isA relationships. More importantly, the tables are valuable because they cover a wide range of topics. Our test dataset contained some 305 randomly selected tables, containing 781 dimensions, which were domain-independent and covering such topics as education, construction, household, travel, and languages. By implementing the algorithms, we found that we are able to extract 92% of isA relationships successfully. Different techniques were implemented to reach the goal of this research. This section of the paper looks deeper into the results to determine their effectiveness. First, the preprocessing step described in Section V was found crucial to 710
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SEKE San Francisco Bay July 1-3 201
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Copyright © 2012 by Knowledge Syst
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The 24 th International Conference
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Zhenyu Chen, Nanjing University, Ch
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Riccardo Martoglia, University of M
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Poster/Demo Sessions Co-Chairs Fars
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Evaluating the Cost-Effectiveness o
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Program Understanding Evaluating Op
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An Empirical Study of Execution-Dat
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Computer Forensics: Toward the Cons
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Modeling Bridging KDM and ASTM for
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A Mapping Study on Software Product
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A proposal for the improvement of t
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Panel on Future Trends of Software
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een available for analysis. Social
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The rest of this paper is orga nize
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Now we assume is given, we first pr
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“iphone” and “blackberry” a
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nique estimate the impact set based
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Figure 1. An example to illustrate
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trategy, the results should support
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Figure 1. System Architecture would
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Figure 2. Sequence Diagram of Train
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deployed on Android phone and runs
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the ontology information is only us
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Both kinds of axioms lead to an inh
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engineer and achieving requirements
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elicitation and the act ivities con
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Step 2. Step 3. 1.2. Use of Procedu
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Figure 6.b. Task: Cognitive Analysi
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original User’s Discourse / Speec
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II. RELATED WORK Business processes
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test, we did not find any significa
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For the indicator “requirements a
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the model checkers SPIN [8] and NuS
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allow for the presentation of syste
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the requirement or design phases sh
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using the CR estimates against the
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Data Set TABLE II. DATA SETS Artifa
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Figure 4. Relative Error in the Est
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Take basic requirements from user a
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sent Semantic Web Ontologies. It co
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The rest of the paper is organized
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and the risk level. Existing assess
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diagram could give developers an in
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Figure 2. Co
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• Sensor(Lexical): at the top and
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An Overview of the RSLingo Approach
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Figure 2. Overview of the RSLingo a
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DETECTING EMERGENT BEHAVIOR IN DIST
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Definition 2: For a set of MSCs M,
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Stability of Filter-Based Feature S
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Algorithm 1: Threshold-Based Featur
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MI KS GM AUC PRC S2N RUS35 RUS50 RO
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80
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82
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84
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86
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Cloud Application Resource Mapping
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Fig. 1. UML Collaboration Diagram m
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Fig. 2. UML Activity Diagram modeli
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An Empirical Study of Software Metr
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TABLE I SOFTWARE DATASETS CHARACTER
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1) Classifiers: In this study, soft
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Progressive Clustering with Learned
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IV. DATA SOURCES We have been worki
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meta-data we assign to indicate sig
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Multi-Objective Optimization of Fuz
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uilding FNNs that satisfy different
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VI. EXPERIMENTAL RESULTS The presen
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Generating Performance Test Scripts
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test duration(s) for the scenario(s
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which are composed by the name and
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A Catalog of Patterns for Concept L
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assisted solution for lattice analy
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The complete lattice of the class s
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Client-Side Rendering Mechanism: A
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JavaScript code directly within the
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Fig. 6. CPU usage percentage of pre
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may introduce the extra learning cu
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[9] but no validation is provided i
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One of the st udents from Group B t
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scribed by the equation: where η i
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4 Model Checking DPN We use HyTech
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Recommender systems are usually cla
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such as one week and gradually dete
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•
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146
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Enforcing Contracts for Aspect-orie
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Table 1. Behavioral Rules in LSD [9
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1 public privileged aspect Update {
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Towards More Generic Aspect-Oriente
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vals must be denoted by some notion
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Aspect-Orientation in the Developme
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Table II SELECTED PRIMARY STUDIES #
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Evaluating Open Source Reverse Engi
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Table 1. Candidate reverse engineer
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Some tools may perform the needed f
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Coordination Model to Support Visua
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this CMV approach we employee three
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colored according to the Gradient T
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Improving Program Comprehension in
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The method used to support and eval
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
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An Approach for Software Component
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properties. In particular, the foll
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Conference Dataset Anatomy Dataset
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equipment maintenance node may cont
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Online Anomaly Detection for Compon
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B. Monitoring Static Method Invocat
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undle which is responsible for pars
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An Exploratory Study of One-Use and
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Figure 1. Mindmap of Reuse Design P
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subject also caused outliers for re
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Choosing licenses in free open sour
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The important aspect is to provide
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that holds a model of each software
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Online Probability Application Char
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TABLE II THE PARAMETERS FOR THE HAR
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[10] N. Gunther, “Hit-and-run tac
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Section 5 d iscusses some related w
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model and the analysis results. The
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tree view of UML model, as show in
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II. BACKGROUND In this section, we
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Algorithm 2 MarkStatements function
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(a) printtok (b) printtok2 (c) sche
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Gupta extended HGS and proposed the
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manage intellectual knowledge with
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D. Manage Training When any trainin
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TABLE II. COMPARISON BETWEEN THE EX
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B. Trace Semantics of DAP Fig. 1. A
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∀ [DR ′ ] []gen [; ] [D]□ [;
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transformation idea in MDA [13] . T
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MappingRule MappingTGtoHP { [Rule I
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executing a continuous transition,
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protocol of DATS we used is properl
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in Boolean search and reasoning has
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to be broken by expelling one of it
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where, among other things: 1. The t
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A never-ending language learner cal
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4. i 2 Learning(DEC, mex) via Bias
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4.4. An Illustrative Example Now le
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Evolutionary Learning and Fuzzy Log
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In addition to the rules, the knowl
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As described in the previous sectio
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the ontology hierarchy; learning a
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three different learning techniques
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TABLE IX. USING SVM FOR LEARNING tr
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Siemens set contains seven C progra
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The program tcas is not included be
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esults. To be more specific, they w
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According to IEEE [10], regression
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(a) Volatility (b) Complexity (c) R
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It mimics the test engineer knowled
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integrate the test results. • Aut
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No tify the cloud controller to get
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of execution-data classification. T
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Figure 1. Boxplots of average AUC r
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anches is more applicable than the
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To validate the proposed approach,
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contains broadcast of Subject to Ob
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S 1a=1; S 2b=5; cobegin { S 3read
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.
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298
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A functionality is the ability of a
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level); in UML/J2EE technology [11]
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• to provide a unified catalogue
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flexibility they provide to model s
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complete in zero-time, and thus hav
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figure assumes the experiment with
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could be a practical way of dealing
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and (iv) the reduced amount of line
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III.BUSINESS RULES FRAMEWORK FOR KN
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explanation for why the program wan
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320
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322
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A model introducing SOAs quality at
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there exist a hierarchical ranking
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Software as a Service: Undo Hernán
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operations consist in reinjection i
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stored. If event failure, external
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ensure users the trustworthiness of
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Patient Doctor Doctor Patient Direc
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going down at a time. This is a rea
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Decidability of Minimal Supports of
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A S 1 1 1 0 0 0 1 1 0 0 0 1 1
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satisfies R( D) R( C) S 1. Compa
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Automated Generation of Concurrent
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target of testing. A blackbox test
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sequence. Each t i θ i in the firi
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SAMAT -AToolforSoftware Architectur
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Figure 4. The SAM Hierarchical Mode
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High-level Petri net Place Place Ty
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verification and thus is often limi
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(r 1 r 2 ...r k ) 1 denotes Path [r
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Fig. 3. Example a 3-compound weakly
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364
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366
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368
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necessary before verification. In t
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CTL formula as: AG (( jp EX( jp ad
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equirements level, like EA-Miner [1
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376
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378
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380
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II. RESOURCE-ORIENTED WORKFLOW MODE
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eventually triggers a co mmon task.
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Input: A resource oriented workflow
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access control exists, the action i
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(ACCDA), is customized to address a
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ICrudSchema and shown in Figure 3.
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Connectors for Secure Software Arch
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Non-repudiation security service pr
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anAsynchronousCustomer InterfaceCon
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How Social Network APIs Have Ended
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OSN Social Feed Unique Features Con
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users can control the reach of thei
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Computer Forensics: Toward the Cons
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In 2009, Cohen et al. in [4] have o
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dcterms:contributor, dcterms:creato
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A Holistic Approach to Software Tra
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Figure 1. Figure 1 shows an overvie
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B. Case study in a proprietary proj
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Pointcut Design with AODL Saqib iqb
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system attributes, methods and exec
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Feature modeling and Verification b
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hasOptionalPart hasPart hasMandato
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A Context Ontology Model for Pervas
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Figure 1. UML view of the model. Th
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Figure 3. Architecture for deliveri
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Ontology-based Representation of Si
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Figure 2. Upper Ontology fragment f
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I2Sim simulation model was transfor
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An ontology-based approach for stor
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fying the equivalences between conc
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• Homonymy conflicts: occur when
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Automatic Generation of Architectur
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we dealt w ith the verticals rules.
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Towards Architectural Evolution thr
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different from replacing it, we dec
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Using FCA-based Change Impact Analy
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Table 2. Impact set of C1,C2,C5 cha
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Figure 3. The PF and PTS results fo
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Forecasting Fault Events in Power D
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which the airport data did not cont
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classified. Recall is the probabili
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Testing Interoperability Security P
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Figure 2. Overview of the Test Gene
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Objective Figure 4. Testing
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A New Approach to Evaluate Path Fea
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k v ( df i ) 0 , it means that the
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Model & Method Feasibility Evaluati
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Structural Testing for Multithreade
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adequate to another criterion. This
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480
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482
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484
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A Tiny Specification Metalanguage W
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C. Syntax Extensions The expressive
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however, does not allow variables f
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derivation dependencies. The third
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model extension as proposed in [6].
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Figure 1. The process for engineeri
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Figure 4. The abstractions extracte
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way to detect something which could
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Algorithm 1: Algorithm to recursive
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obtain the input of experts on desi
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A. Pattern Reuse In patterns reuse
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IV. SCATTER In order to enhance pat
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These latter would be clustered acc
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described or even r etrieved, thus
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DC2AP Element and their Application
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21. Consequences Example of use DC2
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Legacy2Cloud logic. In this scope,
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2.3.1 Complementarity of MDD techno
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3.2.2 EAggregateRelationship The EA
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cess. The proposal follows the ADM
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II. OVERVIEW OF MODAL TRANSITION SY
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(1) V □ (A) ⊆ V □ (B), V ♦
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Suppose P = 〈S P , SP i , AI P ,
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Adaptive software should basically
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A. Step S1 - Define the Business Pr
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F. Step S6 - Monitor at Run-Time Th
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Figure 1, a metamodel defines an XM
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Figure 3 is a feature diagram that
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ecause the system of C-net model is
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Pub-T Tc Tc Sub-T Sub-T Figu
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Visual Studio Achievements, a Visua
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the project. This achievement has t
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proposed in this work requires the
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FTS is an important research area,
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C. Dependent Variables and Measures
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D. Conclusion Validity Conclusion v
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evaluation of team productivity, qu
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Figure 3: Process Fragment Example
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complexity issues traditionally inv
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Fig. 1: Swing Framework Class Diagr
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ize:Class”, with an empty precond
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Fig. 11: Overwritten Method Fig. 12
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Investigating the use of Bayesian n
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process for the network. Thus, this
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Reuse of Experiences Applied to Req
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Step 1: To start the process, the u
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Specification of Safety Critical Sy
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current belief of agents in order t
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Using the Results from a Systematic
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obtained from the categorization of
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that automatically presents the usa
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Improving a Web Usability Inspectio
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Following the experimental methodol
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About the category “Improvements
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Identification Guidelines for the D
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created according to the preview re
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In our analysis, the “Government
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In the following, we present the re
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USE SCENARIO The application Vuelos
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[8] Mayhew, D., “The Usability En
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Interaction State Set Structure Dat
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B. Component Model Fig. 2 show s th
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As a continuation of our research w
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PROMETHEE methods [3] belong to a f
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comparison rule, a value of 1/9 is
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TABLE V: Supermatrix for the exampl
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for knowledge sharing. Based on str
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y applying hash functions to its su
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Empirical Validation of Variability
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III. EXPERIMENTAL STUDY In this sec
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Table III SPEARMAN’S CORRELATION
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A Mapping Study on Software Product
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most relevant sources in software e
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TABLE IV TOOL’S CLASSIFICATION AC
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[2] P. Clements and L. Northrop, So
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and future works. II. BACKGROUND ON
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System Advanced Standard Module A M
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Algorithm 1: Backtracking for MIN-A
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outcomes from such research fields,
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TABLE IV. CONTINUED FROM PREVIOUS P
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contribution of each study was made
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assets, the plugin approach can be
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Figure 3: PlugSPL Feature Model Edi
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contributions from the several acto
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o Guideline 6.4: Softgoal dependenc
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descriptions must also b e configur
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Page 693 and 694: II. THE PROPOSED TAXONOMY The taxon
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Page 703 and 704: C. Correctness of configuration fil
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Page 707 and 708: Tool Support for Anomaly Detection
Page 709 and 710: Figure 1. System overview of the SD
Page 711 and 712: Once the datasets were evaluated us
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Page 717 and 718: Spacemaker: Practical Formal Synthe
Page 719 and 720: Fig. 1. The ecommerce object model.
Page 721 and 722: Fig. 3. Mapping diagram for Figure
Page 723 and 724: A formal support for incremental be
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Page 735 and 736: Automatic Acquisition of isA Relati
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Page 743 and 744: i.d. subsets of cubes focused on se
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Page 747 and 748: A Tool for Visualization of a Knowl
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Page 751 and 752: shown at Figure 5. Objectives are s
Page 753 and 754: Rendering UML Activity Diagrams as
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Page 757 and 758: ecordProblem → A → reproducePro
Page 759 and 760: umlTUowl - A Both Generic and Vendo
Page 761 and 762: The tool’s ability to deal with S
Page 763 and 764: Elem. UML Example D 12 Harmonizing
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With the GDUC approach, we can mode
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Figure 6. Three proposals containin
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764
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766
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Proactive Two Way Mobile Advertisem
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information. If more than one adver
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Users can al so publish adv ertisem
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The COIN Platform: Supporting the M
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A-3
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Checking Contracts for AOP using XP
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Maicon B. da Silveira, 112 Aldo Dag
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Seyedehmehrnaz Mireslami, 70 Takao
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Wei Zhang, 422 Wenbo Zhang, 188 Zhi
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Desmond Greer Eric Gregoire Christi
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Poster/Demo Presenter’s Index A A
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SEKE 2012 Proceedings - Knowledge Systems Institute

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