11,110 research outputs found
What Causes My Test Alarm? Automatic Cause Analysis for Test Alarms in System and Integration Testing
Driven by new software development processes and testing in clouds, system
and integration testing nowadays tends to produce enormous number of alarms.
Such test alarms lay an almost unbearable burden on software testing engineers
who have to manually analyze the causes of these alarms. The causes are
critical because they decide which stakeholders are responsible to fix the bugs
detected during the testing. In this paper, we present a novel approach that
aims to relieve the burden by automating the procedure. Our approach, called
Cause Analysis Model, exploits information retrieval techniques to efficiently
infer test alarm causes based on test logs. We have developed a prototype and
evaluated our tool on two industrial datasets with more than 14,000 test
alarms. Experiments on the two datasets show that our tool achieves an accuracy
of 58.3% and 65.8%, respectively, which outperforms the baseline algorithms by
up to 13.3%. Our algorithm is also extremely efficient, spending about 0.1s per
cause analysis. Due to the attractive experimental results, our industrial
partner, a leading information and communication technology company in the
world, has deployed the tool and it achieves an average accuracy of 72% after
two months of running, nearly three times more accurate than a previous
strategy based on regular expressions.Comment: 12 page
A supplier selection using a hybrid grey based hierarchical clustering and artificial bee colony
Selection of one or a combination of the most suitable potential providers and outsourcing problem is the most important strategies in logistics and supply chain management. In this paper, selection of an optimal combination of suppliers in inventory and supply chain management are studied and analyzed via multiple attribute decision making approach, data mining and evolutionary optimization algorithms. For supplier selection in supply chain, hierarchical clustering according to the studied indexes first clusters suppliers. Then, according to its cluster, each supplier is evaluated through Grey Relational Analysis. Then the combination of suppliersā Pareto optimal rank and costs are obtained using Artificial Bee Colony meta-heuristic algorithm. A case study is conducted for a better description of a new algorithm to select a multiple source of suppliers
Lotsize optimization leading to a -median problem with cardinalities
We consider the problem of approximating the branch and size dependent demand
of a fashion discounter with many branches by a distributing process being
based on the branch delivery restricted to integral multiples of lots from a
small set of available lot-types. We propose a formalized model which arises
from a practical cooperation with an industry partner. Besides an integer
linear programming formulation and a primal heuristic for this problem we also
consider a more abstract version which we relate to several other classical
optimization problems like the p-median problem, the facility location problem
or the matching problem.Comment: 14 page
Privacy-enhancing Aggregation of Internet of Things Data via Sensors Grouping
Big data collection practices using Internet of Things (IoT) pervasive
technologies are often privacy-intrusive and result in surveillance, profiling,
and discriminatory actions over citizens that in turn undermine the
participation of citizens to the development of sustainable smart cities.
Nevertheless, real-time data analytics and aggregate information from IoT
devices open up tremendous opportunities for managing smart city
infrastructures. The privacy-enhancing aggregation of distributed sensor data,
such as residential energy consumption or traffic information, is the research
focus of this paper. Citizens have the option to choose their privacy level by
reducing the quality of the shared data at a cost of a lower accuracy in data
analytics services. A baseline scenario is considered in which IoT sensor data
are shared directly with an untrustworthy central aggregator. A grouping
mechanism is introduced that improves privacy by sharing data aggregated first
at a group level compared as opposed to sharing data directly to the central
aggregator. Group-level aggregation obfuscates sensor data of individuals, in a
similar fashion as differential privacy and homomorphic encryption schemes,
thus inference of privacy-sensitive information from single sensors becomes
computationally harder compared to the baseline scenario. The proposed system
is evaluated using real-world data from two smart city pilot projects. Privacy
under grouping increases, while preserving the accuracy of the baseline
scenario. Intra-group influences of privacy by one group member on the other
ones are measured and fairness on privacy is found to be maximized between
group members with similar privacy choices. Several grouping strategies are
compared. Grouping by proximity of privacy choices provides the highest privacy
gains. The implications of the strategy on the design of incentives mechanisms
are discussed
Variable selection and updating in model-based discriminant analysis for high dimensional data with food authenticity applications
Food authenticity studies are concerned with determining if food samples have been correctly labelled or not. Discriminant analysis methods are an integral part of the methodology for food authentication. Motivated by food authenticity applications, a model-based discriminant analysis method that includes variable selection is presented. The discriminant analysis model is fitted in a semi-supervised manner using both labeled and unlabeled data. The method is shown to give excellent classification
performance on several high-dimensional multiclass food authenticity datasets with more variables than observations. The variables selected by the proposed method provide information about which variables are meaningful for classification purposes. A headlong search strategy for variable selection is shown to be efficient in terms of computation and achieves excellent classification performance. In applications to several food authenticity datasets, our proposed method outperformed default implementations of Random Forests, AdaBoost, transductive SVMs and Bayesian Multinomial Regression by substantial margins
- ā¦