Academic literature on the topic 'Traces clustering'
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Journal articles on the topic "Traces clustering"
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Greco, G., A. Guzzo, L. Pontieri, and D. Sacca. "Discovering expressive process models by clustering log traces." IEEE Transactions on Knowledge and Data Engineering 18, no. 8 (August 2006): 1010–27. http://dx.doi.org/10.1109/tkde.2006.123.
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Wu, Jianhong, Hossein Zivari-Piran, John D. Hunter, and John G. Milton. "Projective Clustering Using Neural Networks with Adaptive Delay and Signal Transmission Loss." Neural Computation 23, no. 6 (June 2011): 1568–604. http://dx.doi.org/10.1162/neco_a_00124.
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We develop a new neural network architecture for projective clustering of data sets that incorporates adaptive transmission delays and signal transmission information loss. The resultant selective output signaling mechanism does not require the addition of multiple hidden layers but instead is based on the assumption that the signal transmission velocity between input processing neurons and clustering neurons is proportional to the similarity between the input pattern and the feature vector (the top-down weights) of the clustering neuron. The mathematical model governing the evolution of the signal transmission delay, the short-term memory traces, and the long-term memory traces represents a new class of large-scale delay differential equations where the evolution of the delay is described by a nonlinear differential equation involving the similarity measure already noted. We give a complete description of the computational performance of the network for a wide range of parameter values.
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Gomez, Gibran, Platon Kotzias, Matteo Dell’Amico, Leyla Bilge, and Juan Caballero. "Unsupervised Detection and Clustering of Malicious TLS Flows." Security and Communication Networks 2023 (January 12, 2023): 1–17. http://dx.doi.org/10.1155/2023/3676692.
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Malware abuses TLS to encrypt its malicious traffic, preventing examination by content signatures and deep packet inspection. Network detection of malicious TLS flows is important, but it is a challenging problem. Prior works have proposed supervised machine learning detectors using TLS features. However, by trying to represent all malicious traffic, supervised binary detectors produce models that are too loose, thus introducing errors. Furthermore, they do not distinguish flows generated by different malware. On the other hand, supervised multiclass detectors produce tighter models and can classify flows by the malware family but require family labels, which are not available for many samples. To address these limitations, this work proposes a novel unsupervised approach to detect and cluster malicious TLS flows. Our approach takes input network traces from sandboxes. It clusters similar TLS flows using 90 features that capture properties of the TLS client, TLS server, certificate, and encrypted payload and uses the clusters to build an unsupervised detector that can assign a malicious flow to the cluster it belongs to, or determine if it is benign. We evaluate our approach using 972K traces from a commercial sandbox and 35M TLS flows from a research network. Our clustering shows very high precision and recall with an F1 score of 0.993. We compare our unsupervised detector with two state-of-the-art approaches, showing that it outperforms both. The false detection rate of our detector is 0.032% measured over four months of traffic.
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Cuzzocrea, Alfredo, Francesco Folino, Massimo Guarascio, and Luigi Pontieri. "Deviance-Aware Discovery of High-Quality Process Models." International Journal on Artificial Intelligence Tools 27, no. 07 (November 2018): 1860009. http://dx.doi.org/10.1142/s0218213018600096.
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Process Discovery techniques, allowing to extract graph-like models from large process logs, are a valuable mean for grasping a summarized view of real business processes’ behaviors. If augmented with statistics on process performances (e.g., processing times), such models help study the evolution of process performances across different processing steps, and possibly detect bottlenecks and worst practices. However, when the process analyzed exhibits complex and heterogeneous behaviors, these techniques fail to yield good quality models, in terms of readability, accuracy and generality. In particular, the presence of deviant traces may lead to cumbersome models and misleading performance statistics. Current noise/outlier filtering solutions can alleviate this problem and help discover a better model for “normal” process executions, but they do not provide insight on the deviant ones. Then, difficult and expensive analyses are usually performed to extract interpretable and general enough patterns for deviant behaviors. The performance-oriented discovery approach proposed here is addressed to recognize and describe both a normal execution scenario and deviant ones for the process analyzed, by inducing different sub-models: (i) a collection of readable clustering rules (conjunctive patterns over trace attributes) defining the deviance scenarios; (ii) a performance model [Formula: see text] for the “normal” traces that do not fall in any deviant scenario; and (iii) a performance model (and a “difference” model emphasizing the differences in behaviors from the “normal” execution scenario), for each discovered deviance scenario. Technically, these models are discovered by exploiting a conceptual clustering method, embedded in an iterative optimization scheme where the current version of [Formula: see text] is replaced with the model extracted from the newly found normality cluster, in case the latter is more accurate than [Formula: see text]; on the other hand, the clustering procedure is devised to greedily find groups of traces that maximally deviate from [Formula: see text]. Tests on real-life logs confirmed the validity of this approach, and its capability to find good performance models, and to support the analysis of deviant process instances.
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Dobrota, Milan, Boris Delibašić, and Pavlos Delias. "A Skiing Trace Clustering Model for Injury Risk Assessment." International Journal of Decision Support System Technology 8, no. 1 (January 2016): 56–68. http://dx.doi.org/10.4018/ijdsst.2016010104.
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This paper investigates the relation between skiing movement activity patterns and risk of injury. The goal is to provide a framework which can be used for estimating the level of skiers' injury risks, based on skiing patterns. Data, collected from ski-lift gates in the form of process event logs is analyzed. After initial transformation of data into traces, trace vectors, and similarity matrix, using several clustering methods different skiing patterns are identified and compared. The quality of clusters is determined by how well clusters discriminate between injured and noninjured skiers. The goal was to achieve the best possible discrimination. Several experimental settings were made to achieve and suggest a good combination of algorithm parameters and cluster number. After clusters are obtained, they are categorized in three categories according to risk level. It can be concluded that the proposed method can be used to distinguish skiing patterns by risk category based on injury occurrences.
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Dong, Zhenfen, Yuheng Men, Zhengming Li, Zhenzhen Liu, and Jianwei Ji. "Chilling Injury Segmentation of Tomato Leaves Based on Fluorescence Images and Improved k-Means++ Clustering." Transactions of the ASABE 64, no. 1 (2021): 13–22. http://dx.doi.org/10.13031/trans.13212.
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HighlightsChlorophyll fluorescence imaging can be used to evaluate chilling injury.Chilling injury area heterogeneity in the L*a*b* color space is significant.Improved k-means++ clustering has a good segmentation effect on chilling injury.Abstract. The application of fluorescence imaging in the detection of tomato chilling injury was investigated. With the segmentation of the chilling injury area serving as the experimental target, an algorithm based on chlorophyll fluorescence image analysis and improved k-means++ clustering was proposed. First, the extraction of lateral heterogeneity values algorithm was used to analyze the horizontal heterogeneity in five color spaces of the fluorescence images of tomato seedling leaves, and it was found that the chilling injury area was significant in the L*a*b* color space. Second, the fluorescence image was converted from the RGB color space to the L*a*b* color space, and the k-means++ algorithm was used to cluster the two-dimensional data of the a*b* space. Third, insertion sorting was used to reorder the different label regions obtained by the k-means++ clustering algorithm, and the region with the largest value was used as the target region. Finally, the binary image of the target region was filtered using a morphological noise filter, and the cold-damaged area was outputted by the mask operation. The results showed that the cold-damaged area was well segmented when the fluorescence imaging contained yellow cold traces. The mean match rate of the proposed algorithm was 37.08%, 13.52%, and 0.96% higher than that based on the HSV model and watershed algorithm, the fuzzy C-means clustering method, and the k-means clustering method, respectively. Similarly, the mean error rate was 13.69%, 5.56%, and 0.16% lower than that based on the HSV model and watershed algorithm, the fuzzy C-means clustering method, and the k-means clustering method, respectively. These findings provide a foundation for research on early warning of chilling injury by identifying the chilling injury status of tomato leaves using a computer vision method. Keywords: Chlorophyll fluorescence, Fluorescence image, Image segmentation, k-Means++.
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Chang, Xiangmao, Quan Wang, Zhiguo Qu, and Yanchao Zhao. "The integration of compressive sensing and clustering for date gathering in unmanned aircraft system–aided networks." International Journal of Distributed Sensor Networks 13, no. 8 (August 2017): 155014771772771. http://dx.doi.org/10.1177/1550147717727713.
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The development of the unmanned aircraft systems is envisioned to greatly reduce the energy consumption of sensor nodes in data gathering process using unmanned aircraft systems as mobile sinks. In traditional sensor networks, compressive sensing and clustering are two key energy-efficient techniques for data gathering. However, how to integrate two techniques into the data gathering for unmanned aircraft system–aided wireless sensor networks effectively is still an open problem. Moreover, most clustering schemes focus on the cluster head selection strategy and simplified the problem of cluster member selection, and most compressive sensing schemes are not integrated with the clustering strategy. To this end, this article studies the problem of integrating compressive sensing with clustering for data gathering in unmanned aircraft system–aided networks. We first give a theoretical formulation of this problem. Considering the non-deterministic polynomial-time hard complexity of the problem, we present two algorithms by jointly considering the compressive ratio variation factor and the distance factor to find near-optimal solutions heuristically. Evaluations based on real data traces show that the proposed algorithms greatly reduced the energy consumption of sensor nodes efficiency.
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NAKAZATO, Junji, Jungsuk SONG, Masashi ETO, Daisuke INOUE, and Koji NAKAO. "A Novel Malware Clustering Method Using Frequency of Function Call Traces in Parallel Threads." IEICE Transactions on Information and Systems E94-D, no. 11 (2011): 2150–58. http://dx.doi.org/10.1587/transinf.e94.d.2150.
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N, Pushpalatha M., and runalini M. "Clustering The Duplicate Open Crash Reports Based on Call Stack Traces of Crash Reports." International Journal of Computer Sciences and Engineering 6, no. 9 (September 30, 2018): 207–10. http://dx.doi.org/10.26438/ijcse/v6i9.207210.
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Rojas, Alexis, Gregory P. Dietl, Michał Kowalewski, Roger W. Portell, Austin Hendy, and Jason K. Blackburn. "Spatial point pattern analysis of traces (SPPAT): An approach for visualizing and quantifying site-selectivity patterns of drilling predators." Paleobiology 46, no. 2 (May 2020): 259–71. http://dx.doi.org/10.1017/pab.2020.15.
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AbstractSite-selectivity analysis of drilling predation traces may provide useful behavioral information concerning a predator interacting with its prey. However, traditional approaches exclude some spatial information (i.e., oversimplified trace position) and are dependent on the scale of analysis (e.g., arbitrary grid system used to divide the prey skeleton into sectors). Here we introduce the spatial point pattern analysis of traces (SPPAT), an approach for visualizing and quantifying the distribution of traces on shelled invertebrate prey, which includes improved collection of spatial information inherent to drillhole location (morphometric-based estimation), improved visualization of spatial trends (kernel density and hotspot mapping), and distance-based statistics for hypothesis testing (K-, L-, and pair correlation functions). We illustrate the SPPAT approach through case studies of fossil samples, modern beach-collected samples, and laboratory feeding trials of naticid gastropod predation on bivalve prey. Overall results show that kernel density and hotspot maps enable visualization of subtle variations in regions of the shell with higher density of predation traces, which can be combined with the maximum clustering distance metric to generate hypotheses on predatory behavior and anti-predatory responses of prey across time and geographic space. Distance-based statistics also capture the major features in the distribution of traces across the prey skeleton, including aggregated and segregated clusters, likely associated with different combinations of two modes of drilling predation, edge and wall drilling. The SPPAT approach is transferable to other paleoecologic and taphonomic data such as encrustation and bioerosion, allowing for standardized investigation of a wide range of biotic interactions.
Dissertations / Theses on the topic "Traces clustering"
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Iegorov, Oleg. "Une approche de fouille de données pour le débogage temporel des applications embarquées de streaming." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAM032/document.
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Le déboggage des applications de streaming qui s'exécutent sur les systèmes embarqués multimédia est l'un des domaines les plus exigeants dans le développement de logiciel embarqué. Les nouvelles générations de materiel embarqué introduisent de nouvelles systèmes sur une puce, qui fait que les développeurs du logiciel doivent adapter leurs logiciels aux nouvelles platformes. Le logiciel embarqué doit non seulement fournir des résultats corrects mais aussi le faire en temps réel afin de respecter les propriétés de qualité de service (Quality-of-Service, QoS) du système. Lorsque les propriétés QoS ne sont pas respectées, des bugs temporels font leur apparition. Ces bugs se manifestent comme, par exemple, des glitches dans le flux vidéo ou des craquements dans le flux audio. Le déboggage temporel est en général difficile à effectuer car les bugs temporels n'ont pas souvent de rapport avec l'exactitude fonctionnelle du code des applications, ce qui rend les outils de débogage traditionels, comme GDB, peu utiles. Le non-respect des propriétés QoS peut provenir des interactions entre les applications, ou entre les applications et les processus systèmes. Par conséquent, le contexte d'exécution entier doit être pris en compte pour le déboggage temporel. Les avancements récents en collecte des traces d'exécution permettent aux développeurs de recueillir des traces et de les analyser après la fin d'exécution pour comprendre quelle activité système est responsable des bugs temporels. Cependant, les traces d'exécution ont une taille conséquente, ce qui demande aux devéloppeurs des connaissainces en analyse de données qu'ils n’ont souvent pas.Dans cette thèse, nous proposons SATM - une approche novatrice pour le déboggage temporel des applications de streaming. SATM repose sur la prémisse que les applications sont conçues avec le modèle dataflow, i.e. peuvent être représentées comme un graphe orienté où les données sont transmises entre des unités de calcul (fontions, modules, etc.) appelées "acteurs". Les acteurs doivent être exécutés de manière périodique afin de respecter les propriétés QoS représentées par les contraintes de temps-réél. Nous montrons qu'un acteur qui ne respecte pas de façon répétée sa période pendant l'exécution de l'application cause la violation des contraintes temps-reel de l'application. En pratique, SATM est un workflow d'analyse de données venant des traces d'exécution qui combine des mesures statistiques avec des algorithmes de fouille de données. SATM fournit une méthode automatique du débogage temporel des applications de streaming. Notre approche prend en entrée une trace d'exécution d'une application ayant une QoS basse ainsi qu'une liste de ses acteurs, et tout d'abord détecte des invocations des acteurs dans la trace. SATM découvre ensuite les périodes des acteurs ainsi que les séctions de la trace où la période n'a pas été respectée. Enfin, ces séctions sont analysées afin d'extraire des motifs de l'activité système qui différencient ces sections des autres séctions de la trace. De tels motifs peuvent donner des indices sur l'origine du problème temporel dans le systeme et sont rendus au devéloppeur. Plus précisément, nous représentons ces motifs comme des séquences contrastes minimales et nous étudions des différentes solutions pour fouiller ce type de motifs à partir des traces d'exécution.Enfin, nous montrons la capacité de SATM de détecter une perturbation temporelle injectée artificiellement dans un framework multimedia GStreamer, ainsi que des bugs temporels dans deux cas d'utilisation des applications de streaming industrielles provenant de la société STMicroelectronics. Nous fournissons également une analyse détaillée des algorithmes de fouille de motifs séquentiels appliqués sur les données venant des traces d'exécution, et nous expliquons pour quelle est la raison les algorithmes de pointe n'arrivent pas à fouiller les motifs séquentiels à partir des traces d'exécution de façon efficaceDebugging streaming applications run on multimedia embedded systems found in modern consumer electronics (e.g. in set-top boxes, smartphones, etc) is one of the most challenging areas of embedded software development. With each generation of hardware, more powerful and complex Systems-on-Chip (SoC) are released, and developers constantly strive to adapt their applications to these new platforms. Embedded software must not only return correct results but also deliver these results on time in order to respect the Quality-of-Service (QoS) properties of the entire system. The non-respect of QoS properties lead to the appearance of temporal bugs which manifest themselves in multimedia embedded systems as, for example, glitches in the video or cracks in the sound. Temporal debugging proves to be tricky as temporal bugs are not related to the functional correctness of the code, thus making traditional GDB-like debuggers essentially useless. Violations of QoS properties can stem from complex interactions between a particular application and the system or other applications; the complete execution context must be, therefore, taken into account in order to perform temporal debugging. Recent advances in tracing technology allow software developers to capture a trace of the system's execution and to analyze it afterwards to understand which particular system activity is responsible for the violations of QoS properties. However, such traces have a large volume, and understanding them requires data analysis skills that are currently out of the scope of the developers' education.In this thesis, we propose SATM (Streaming Application Trace Miner) - a novel temporal debugging approach for embedded streaming applications. SATM is based on the premise that such applications are designed under the dataflow model of computation, i.e. as a directed graph where data flows between computational units called actors. In such setting, actors must be scheduled in a periodic way in order to meet QoS properties expressed as real-time constraints, e.g. displaying 30 video frames per second. We show that an actor which does not eventually respect its period at runtime causes the violation of the application’s real-time constraints. In practice, SATM is a data analysis workflow combining statistical measures and data mining algorithms. It provides an automatic solution to the problem of temporal debugging of streaming applications. Given an execution trace of a streaming application exhibiting low QoS as well as a list of its actors, SATM firstly determines exact actors’ invocations found in the trace. It then discovers the actors’ periods, as well as parts of the trace in which the periods are not respected. Those parts are further analyzed to extract patterns of system activity that differentiate them from other parts of the trace. Such patterns can give strong hints on the origin of the problem and are returned to the developer. More specifically, we represent those patterns as minimal contrast sequences and investigate various solutions to mine such sequences from execution trace data.Finally, we demonstrate SATM’s ability to detect both an artificial perturbation injected in an open source multimedia framework, as well as temporal bugs from two industrial use cases coming from STMicroelectronics. We also provide an extensive analysis of sequential pattern mining algorithms applied on execution trace data and explain why state-of-the-art algorithms fail to efficiently mine sequential patterns from real-world traces
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Teboul, Bruno. "Le développement du neuromarketing aux Etats-Unis et en France. Acteurs-réseaux, traces et controverses." Thesis, Paris Sciences et Lettres (ComUE), 2016. http://www.theses.fr/2016PSLED036/document.
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Notre travail de recherche explore de manière comparée le développement du neuromarketing aux Etats-Unis et en France. Nous commençons par analyser la littérature sur le neuromarketing. Nous utilisons comme cadre théorique et méthodologique l’Actor Network Theory (ANT) ou Théorie de l’Acteur-Réseau (dans le sillage des travaux de Bruno Latour et Michel Callon). Nous montrons ainsi comment des actants « humains et non-humains »: acteurs-réseaux, traces (publications) et controverses forment les piliers d’une nouvelle discipline telle que le neuromarketing. Notre approche hybride « qualitative-quantitative », nous permet de construire une méthodologie appliquée de l’ANT: analyse bibliométrique (Publish Or Perish), text mining, clustering et analyse sémantique de la littérature scientifique et web du neuromarketing. A partir de ces résultats, nous construisons des cartographies, sous forme de graphes en réseau (Gephi) qui révèlent les interrelations et les associations entre acteurs, traces et controverses autour du neuromarketingOur research explores the comparative development of neuromarketing between the United States and France. We start by analyzing the literature on neuromarketing. We use as theoretical and methodological framework the Actor Network Theory (ANT) (in the wake of the work of Bruno Latour and Michel Callon). We show how “human and non-human” entities (“actants”): actor-network, traces (publications) and controversies form the pillars of a new discipline such as the neuromarketing. Our hybrid approach “qualitative-quantitative” allows us to build an applied methodology of the ANT: bibliometric analysis (Publish Or Perish), text mining, clustering and semantic analysis of the scientific literature and web of the neuromarketing. From these results, we build data visualizations, mapping of network graphs (Gephi) that reveal the interrelations and associations between actors, traces and controversies about neuromarketing
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Hamdi, Marwa. "Modélisation des processus utilisateurs à partir des traces d’exécution, application aux systèmes d’information faiblement structurés." Electronic Thesis or Diss., La Rochelle, 2022. http://www.theses.fr/2022LAROS036.
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Ce travail porte sur l’analyse des parcours utilisateurs dans les bibliothèques numériques qui se caractérisent par des processus métier faiblement structurés. Nous allons avoir recours à la fouille de processus afin d’extraire les modèles à partir de ces parcours. Les modèles découverts permettront aux concepteurs de ces systèmes de répondre d’une manière plus efficace aux besoins des utilisateurs et à leurs présenter un ensemble de recommandations. Dans cette thèse, nous avons généré des modèles à partir des traces de navigation des utilisateurs du portail web de la bibliothèque nationale de France, Gallica. Dans un premier temps, nous avons adapté ces traces sous un format compatible avec les algorithmes de fouille de processus. Dans un second temps, nous avons regroupé ces traces. L’originalité de notre apport porte sur le regroupement des parcours similaires, en tenant compte des caractéristiques existantes dans les traces, afin d’éviter la génération des modèles complexes, souvent non exploitables, à partir de telles traces volumineuse set non structurées. Enfin, nous avons validé notre méthode sur deux jeux de données simulé et réel. Nous avons comparé notre méthode à deux méthodes inspirées des travaux existants et les résultats montrent que notre méthode surpasse celles existantes sur les deux jeux de données à la fois dans le regroupement et la modélisationThis research focuses on extracting users’ journeys in a digital library characterized by weakly structured business processes. In this thesis, we investigate whether it is possible to model user journeys using process mining. The discovered models allow system designers to respond more efficiently to users’ needs and to present them with a set of recommendations. For our study, we have chosen to extract the users’ journey models of the digital library Gallica, based on real traces generated by their users. First, we adapt these browsing traces in a well-defined format compatible with process mining techniques. The originality of our contribution concerns the grouping of similar paths, considering the existing characteristics in the traces, to avoid the generation of complex models, often not exploitable, from such voluminous and unstructured traces. Finally, we validate our method on two simulated and real data sets. We compare our method to two other methods inspired by existing works. The results show that our method outper forms the existing ones on both datasets in clustering and modeling
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Mauss, Benoit. "Réactions élastiques et inélastiques résonantes pour la caractérisation expérimentale de la cible active ACTAR TPC." Thesis, Normandie, 2018. http://www.theses.fr/2018NORMC226/document.
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ACTAR TPC (ACtive TARget and Time Projection Chamber) est une cible active de nouvelle génération construite au GANIL (Grand Accélérateur d'Ions Lourds). Les cibles actives sont des cibles gazeuses où le gaz permet de détecter le passage de particules chargées selon le principe des chambres à projection temporelle (TPC). La TPC d'ACTAR est formée d'une anode segmentée de 16384 pixels carrés de 2 mm de côté. La haute densité de voies est gérée par le système électronique GET (General Electronics for TPCs). Ce système digitalise également les signaux sur un intervalle de temps donné, pour une reconstruction 3D complète des évènements. Un démonstrateur huit fois plus petit a d'abord été construit pour vérifier le fonctionnement de l’électronique et la conception mécanique. La finalisation d’ACTAR TPC s’est basée sur les résultats du démonstrateur, qui a été testé avec des faisceaux de 6Li, de 24Mg et de 58Ni. Le commissioning d'ACTAR TPC a été effectué dans le cas de la diffusion résonante sur cible de protons avec des faisceaux de 18O et de 20Ne.Un algorithme de reconstruction des traces mesurées dans la TPC permet d'en extraire les angles et les énergies des ions impliqués dans les réactions. Les résultats sont comparés à des données connues pour déterminer les performances du système de détection. Les résolutions obtenues sur le commissioning à partir de calculs en matrice R valident l'utilisation d'ACTAR TPC pour de futures expériences. Par ailleurs, la diffusion résonante 6Li + 4He réalisée avec le démonstrateur a permis d'étudier les états d’agrégat alpha dans le 10B. Deux résonances à 8.58 MeV et 9.52 MeV sont observées pour la première fois en diffusion élastique dans cette voie de réactionACTAR TPC (ACtive TARget and Time Projection Chamber) is a next generation active target that was designed and built at GANIL (Grand Accélérateur d'Ions Lourds). Active targets are gaseous targets in which the gas is also used to track charged particles following the principles of time projection chambers (TPC). The TPC of ACTAR has a segmented anode of 16384 2 mm side square pixels. The high density of pixels is processed using the GET (General Electronics for TPCs) electronic system. This system also digitizes the signals over a time interval, enabling a full 3D event reconstruction. An eight time smaller demonstrator was first built to verify the electronics operation and the mechanical design. ACTAR TPC's final design was based on results obtained with the demonstrator which was tested using 6Li, 24Mg and 58Ni beams. The commissioning of ACTAR TPC was then carried out for the case of resonant scattering on a proton target using 18O and 20Ne beams. A track reconstruction algorithm is used to extract the angles and energies of the ions involved in the reactions. Results are compared to previous data to determine the detection system performances. Comparing the commissioning data with R matrix calculations, excitation functions resolutions in different cases are obtained. The use of ACTAR TPC is validated for future experiments. Furthermore, alpha clustering was studied in 10B through the resonant scattering 6Li + 4He, carried out with the demonstrator. Two resonances at 8.58 MeV and 9.52 MeV are observed for the first time in elastic scattering with this reaction channel
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Lallouache, Mehdi. "Clustering in foreign exchange markets : price, trades and traders." Thesis, Châtenay-Malabry, Ecole centrale de Paris, 2015. http://www.theses.fr/2015ECAP0040/document.
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En utilisant des données haute-fréquence inédites, cette thèse étudie trois types de regroupements (“clusters”) présents dans le marché des changes: la concentration d'ordres sur certains prix, la concentration des transactions dans le temps et l'existence de groupes d'investisseurs prenant les mêmes décisions. Nous commençons par étudier les propriétés statistiques du carnet d'ordres EBS pour les paires de devises EUR/USD et USD/JPY et l'impact d'une réduction de la taille du tick sur sa dynamique. Une grande part des ordres limites est encore placée sur les anciens prix autorisés, entraînant l'apparition de prix-barrières, où figurent les meilleures limites la plupart du temps. Cet effet de congestion se retrouve dans la forme moyenne du carnet où des pics sont présents aux distances entières. Nous montrons que cette concentration des prix est causée par les traders manuels qui se refusent d’utiliser la nouvelle résolution de prix. Les traders automatiques prennent facilement la priorité, en postant des ordres limites un tick devant les pics de volume.Nous soulevons ensuite la question de l'aptitude des processus de Hawkes à rendre compte de la dynamique du marché. Nous analysons la précision de tels processus à mesure que l'intervalle de calibration est augmenté. Différent noyaux construits à partir de sommes d'exponentielles sont systématiquement comparés. Le marché FX qui ne ferme jamais est particulièrement adapté pour notre but, car il permet d’éviter les complications dues à la fermeture nocturne des marchés actions. Nous trouvons que la modélisation est valide selon les trois tests statistiques, si un noyau à deux exponentielles est utilisé pour fitter une heure, et deux ou trois pour une journée complète. Sur de plus longues périodes la modélisation est systématiquement rejetée par les tests à cause de la non-stationnarité du processus endogène. Les échelles de temps d'auto-excitation estimées sont relativement courtes et le facteur d'endogénéité est élevé mais sous-critique autour de 0.8. La majorité des modèles à agents suppose implicitement que les agents interagissent à travers du prix des actifs et des volumes échangés. Certains utilisent explicitement un réseau d'interaction entre traders, sur lequel des rumeurs se propagent, d'autres, un réseau qui représente des groupes prenant des décisions communes. Contrairement à d'autres types de données, de tels réseaux, s'ils existent, sont nécessairement implicites, ce qui rend leur détection compliquée. Nous étudions les transactions des clients de deux fournisseur de liquidités sur plusieurs années. En supposant que les liens entre agents sont déterminés par la synchronisation de leur activité ou inactivité, nous montrons que des réseaux d'interactions existent. De plus, nous trouvons que l'activité de certains agents entraîne systématiquement l’activité d'autres agents, définissant ainsi des relations de type “lead-lag” entre les agents. Cela implique que le flux des clients est prévisible, ce que nous vérifions à l'aide d'une méthode sophistiquée d'apprentissage statistiqueThe aim of this thesis is to study three types of clustering in foreign exchange markets, namely in price, trades arrivals and investors decisions. We investigate the statistical properties of the EBS order book for the EUR/USD and USD/JPY currency pairs and the impact of a ten-fold tick size reduction on its dynamics. A large fraction of limit orders are still placed right at or halfway between the old allowed prices. This generates price barriers where the best quotes lie for much of the time, which causes the emergence of distinct peaks in the average shape of the book at round distances. Furthermore, we argue that this clustering is mainly due to manual traders who remained set to the old price resolution. Automatic traders easily take price priority by submitting limit orders one tick ahead of clusters, as shown by the prominence of buy (sell) limit orders posted with rightmost digit one (nine).The clustering of trades arrivals is well-known in financial markets and Hawkes processes are particularly suited to describe this phenomenon. We raise the question of what part of market dynamics Hawkes processes are able to account for exactly. We document the accuracy of such processes as one varies the time interval of calibration and compare the performance of various types of kernels made up of sums of exponentials. Because of their around-the-clock opening times, FX markets are ideally suited to our aim as they allow us to avoid the complications of the long daily overnight closures of equity markets. One can achieve statistical significance according to three simultaneous tests provided that one uses kernels with two exponentials for fitting an hour at a time, and two or three exponentials for full days, while longer periods could not be fitted within statistical satisfaction because of the non-stationarity of the endogenous process. Fitted timescales are relatively short and endogeneity factor is high but sub-critical at about 0.8.Most agent-based models of financial markets implicitly assume that the agents interact through asset prices and exchanged volumes. Some of them add an explicit trader-trader interaction network on which rumors propagate or that encode groups that take common decisions. Contrarily to other types of data, such networks, if they exist, are necessarily implicit, which makes their determination a more challenging task. We analyze transaction data of all the clients of two liquidity providers, encompassing several years of trading. By assuming that the links between agents are determined by systematic simultaneous activity or inactivity, we show that interaction networks do exist. In addition, we find that the (in)activity of some agents systematically triggers the (in)activity of other traders, defining lead-lag relationships between the agents. This implies that the global investment flux is predictable, which we check by using sophisticated machine learning methods
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Abonyi, J., FD Tamás, S. Potgieter, and H. Potgieter. "Analysis of Trace Elements in South African Clinkers using Latent Variable Model and Clustering." South African Journal of Chemistry, 2003. http://encore.tut.ac.za/iii/cpro/DigitalItemViewPage.external?sp=1000893.
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The trace element content of clinkers (and possibly of cements) can be used to identify the manufacturing factory. The Mg, Sr, Ba,
Mn, Ti, Zr, Zn and V content of clinkers give detailed information for the determination of the origin of clinkers produced in
different factories. However, for the analysis of such complex data there is a need for algorithmic tools for the visualization and
clustering of the samples. This paper proposes a new approach for this purpose. The analytical data are transformed into a twodimensional
latent space by factor analysis (probabilistic principal component analysis) and dendograms are constructed for
cluster formation. The classification of South African clinkers is used as an illustrative example for the approach.
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Petraro, Alessandro. "Clustering di tracce di mobilità per l’identificazione di stili di guida." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13003/.
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Traffic simulators are effective tools to support decisions in urban planning systems, to identify criticalities, to observe emerging behaviours in road networks and to configure road infrastructures, such as road side units and traffic lights. Clearly the more realistic the simulator the more precise the insight provided to decision makers. This paper provides a first step toward the design and calibration of traffic micro-simulator to produce realistic behaviour. The long term idea is to collect and analyse real traffic traces collecting vehicular information, to cluster them in groups representing similar driving behaviours and then to extract from these clusters relevant parameters to tune the microsimulator. In this paper we have run controlled experiments where traffic traces have been synthetized to obtain different driving styles, so that the effectiveness of the clustering algorithm could be checked on known labels. We describe the overall methodology and the results already achieved on the controlled experiment, showing the clusters obtained and reporting guidelines for future experiments.
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Abonyia, J., FD Tamas, and S. Potgieter. "Analysis of trace elements in South African clinkers using latent variable model and clustering." South African Journal of Chemistry, 2003. http://encore.tut.ac.za/iii/cpro/DigitalItemViewPage.external?sp=1001952.
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Abstract
The trace element content of clinkers (and possibly of cements) can be used to identify the manufacturing factory. The Mg, Sr, Ba,
Mn, Ti, Zr, Zn and V content of clinkers give detailed information for the determination of the origin of clinkers produced in
different factories. However, for the analysis of such complex data there is a need for algorithmic tools for the visualization and
clustering of the samples. This paper proposes a new approach for this purpose. The analytical data are transformed into a twodimensional
latent space by factor analysis (probabilistic principal component analysis) and dendograms are constructed for
cluster formation. The classification of South African clinkers is used as an illustrative example for the approach.
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Qin, Tian. "Estimation of Water Demands Using an MCMC Algorithm with Clustering Methods." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1544002222852385.
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Liang, Xuwei. "MODELING AND QUANTITATIVE ANALYSIS OF WHITE MATTER FIBER TRACTS IN DIFFUSION TENSOR IMAGING." UKnowledge, 2011. http://uknowledge.uky.edu/gradschool_diss/818.
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Diffusion tensor imaging (DTI) is a structural magnetic resonance imaging (MRI) technique to record incoherent motion of water molecules and has been used to detect micro structural white matter alterations in clinical studies to explore certain brain disorders. A variety of DTI based techniques for detecting brain disorders and facilitating clinical group analysis have been developed in the past few years. However, there are two crucial issues that have great impacts on the performance of those algorithms. One is that brain neural pathways appear in complicated 3D structures which are inappropriate and inaccurate to be approximated by simple 2D structures, while the other involves the computational efficiency in classifying white matter tracts.
The first key area that this dissertation focuses on is to implement a novel computing scheme for estimating regional white matter alterations along neural pathways in 3D space. The mechanism of the proposed method relies on white matter tractography and geodesic distance mapping. We propose a mask scheme to overcome the difficulty to reconstruct thin tract bundles. Real DTI data are employed to demonstrate the performance of the pro- posed technique. Experimental results show that the proposed method bears great potential to provide a sensitive approach for determining the white matter integrity in human brain.
Another core objective of this work is to develop a class of new modeling and clustering techniques with improved performance and noise resistance for separating reconstructed white matter tracts to facilitate clinical group analysis. Different strategies are presented to handle different scenarios. For whole brain tractography reconstructed white matter tracts, a Fourier descriptor model and a clustering algorithm based on multivariate Gaussian mixture model and expectation maximization are proposed. Outliers are easily handled in this framework. Real DTI data experimental results show that the proposed algorithm is relatively effective and may offer an alternative for existing white matter fiber clustering methods. For a small amount of white matter fibers, a modeling and clustering algorithm with the capability of handling white matter fibers with unequal length and sharing no common starting region is also proposed and evaluated with real DTI data.
Books on the topic "Traces clustering"
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Benestad, Rasmus. Climate in the Barents Region. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.655.
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The Barents Sea is a region of the Arctic Ocean named after one of its first known explorers (1594–1597), Willem Barentsz from the Netherlands, although there are accounts of earlier explorations: the Norwegian seafarer Ottar rounded the northern tip of Europe and explored the Barents and White Seas between 870 and 890 ce, a journey followed by a number of Norsemen; Pomors hunted seals and walruses in the region; and Novgorodian merchants engaged in the fur trade. These seafarers were probably the first to accumulate knowledge about the nature of sea ice in the Barents region; however, scientific expeditions and the exploration of the climate of the region had to wait until the invention and employment of scientific instruments such as the thermometer and barometer. Most of the early exploration involved mapping the land and the sea ice and making geographical observations. There were also many unsuccessful attempts to use the Northeast Passage to reach the Bering Strait. The first scientific expeditions involved F. P. Litke (1821±1824), P. K. Pakhtusov (1834±1835), A. K. Tsivol’ka (1837±1839), and Henrik Mohn (1876–1878), who recorded oceanographic, ice, and meteorological conditions.The scientific study of the Barents region and its climate has been spearheaded by a number of campaigns. There were four generations of the International Polar Year (IPY): 1882–1883, 1932–1933, 1957–1958, and 2007–2008. A British polar campaign was launched in July 1945 with Antarctic operations administered by the Colonial Office, renamed as the Falkland Islands Dependencies Survey (FIDS); it included a scientific bureau by 1950. It was rebranded as the British Antarctic Survey (BAS) in 1962 (British Antarctic Survey History leaflet). While BAS had its initial emphasis on the Antarctic, it has also been involved in science projects in the Barents region. The most dedicated mission to the Arctic and the Barents region has been the Arctic Monitoring and Assessment Programme (AMAP), which has commissioned a series of reports on the Arctic climate: the Arctic Climate Impact Assessment (ACIA) report, the Snow Water Ice and Permafrost in the Arctic (SWIPA) report, and the Adaptive Actions in a Changing Arctic (AACA) report.The climate of the Barents Sea is strongly influenced by the warm waters from the Norwegian current bringing heat from the subtropical North Atlantic. The region is 10°C–15°C warmer than the average temperature on the same latitude, and a large part of the Barents Sea is open water even in winter. It is roughly bounded by the Svalbard archipelago, northern Fennoscandia, the Kanin Peninsula, Kolguyev Island, Novaya Zemlya, and Franz Josef Land, and is a shallow ocean basin which constrains physical processes such as currents and convection. To the west, the Greenland Sea forms a buffer region with some of the strongest temperature gradients on earth between Iceland and Greenland. The combination of a strong temperature gradient and westerlies influences air pressure, wind patterns, and storm tracks. The strong temperature contrast between sea ice and open water in the northern part sets the stage for polar lows, as well as heat and moisture exchange between ocean and atmosphere. Glaciers on the Arctic islands generate icebergs, which may drift in the Barents Sea subject to wind and ocean currents.The land encircling the Barents Sea includes regions with permafrost and tundra. Precipitation comes mainly from synoptic storms and weather fronts; it falls as snow in the winter and rain in the summer. The land area is snow-covered in winter, and rivers in the region drain the rainwater and meltwater into the Barents Sea. Pronounced natural variations in the seasonal weather statistics can be linked to variations in the polar jet stream and Rossby waves, which result in a clustering of storm activity, blocking high-pressure systems. The Barents region is subject to rapid climate change due to a “polar amplification,” and observations from Svalbard suggest that the past warming trend ranks among the strongest recorded on earth. The regional change is reinforced by a number of feedback effects, such as receding sea-ice cover and influx of mild moist air from the south.
Book chapters on the topic "Traces clustering"
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Evermann, Joerg, Tom Thaler, and Peter Fettke. "Clustering Traces Using Sequence Alignment." In Business Process Management Workshops, 179–90. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42887-1_15.
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Koschmider, Agnes. "Clustering Event Traces by Behavioral Similarity." In Lecture Notes in Computer Science, 36–42. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70625-2_4.
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Brun, Anders, Hans Knutsson, Hae-Jeong Park, Martha E. Shenton, and Carl-Fredrik Westin. "Clustering Fiber Traces Using Normalized Cuts." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004, 368–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30135-6_45.
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Richter, Florian, Ludwig Zellner, Janina Sontheim, and Thomas Seidl. "Model-Aware Clustering of Non-conforming Traces." In Lecture Notes in Computer Science, 193–200. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33246-4_12.
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Sun, Yaguang, and Bernhard Bauer. "A Novel Top-Down Approach for Clustering Traces." In Advanced Information Systems Engineering, 331–45. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19069-3_21.
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Greco, Gianluigi, Antonella Guzzo, Luigi Pontieri, and Domenico Saccà. "Mining Expressive Process Models by Clustering Workflow Traces." In Advances in Knowledge Discovery and Data Mining, 52–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24775-3_8.
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Mihăescu, Marian Cristian, Alexandru Virgil Tănasie, Mihai Dascalu, and Stefan Trausan-Matu. "Extracting Patterns from Educational Traces via Clustering and Associated Quality Metrics." In Artificial Intelligence: Methodology, Systems, and Applications, 109–18. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44748-3_11.
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De Weerdt, Jochen. "Trace Clustering." In Encyclopedia of Big Data Technologies, 1–6. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-63962-8_91-1.
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Weerdt, Jochen De. "Trace Clustering." In Encyclopedia of Big Data Technologies, 1706–11. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-77525-8_91.
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Chatain, Thomas, Josep Carmona, and Boudewijn van Dongen. "Alignment-Based Trace Clustering." In Conceptual Modeling, 295–308. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69904-2_24.
Full textConference papers on the topic "Traces clustering"
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R. Neubaer, Thais, Marcelo Fantinato, and Sarajane M. Peres. "Interactive trace clustering." In XV Simpósio Brasileiro de Sistemas de Informação. Sociedade Brasileira de Computação (SBC), 2019. http://dx.doi.org/10.5753/sbsi.2019.7438.
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Process mining aims to automatically discover, analyze and improve business processes. Trace clustering is a task commonly used to reduce the inherent complexity of processes by identifying patterns. This research focuses on the application of experts knowledge in process mining through interactive clustering, referred to herein as interactive trace clustering. The aim is to improve trace clustering by reducing potential losses arising from arbitrary assumptions on the similarity between the datapoints, what is commonly required in unsupervised scenarios. Initial experiments considered partitioning clustering and three representation schemes for traces. Preliminary results show potential to improve the trace clustering quality by inserting experts knowledge.
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Sarvani, A., B. Venugopal, and D. Nagaraju. "Clustering the polymorphic malware traces." In 2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET). IEEE, 2017. http://dx.doi.org/10.1109/icammaet.2017.8186641.
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Luna, Mateus Alex dos Santos, André Paulino Lima, Thaís Rodrigues Neubauer, Marcelo Fantinato, and Sarajane Marques Peres. "Vector space models for trace clustering: a comparative study." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/eniac.2021.18274.
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Process mining explores event logs to offer valuable insights to business process managers. Some types of business processes are hard to mine, including unstructured and knowledge-intensive processes. Then, trace clustering is usually applied to event logs aiming to break it into sublogs, making it more amenable to the typical process mining task. However, applying clustering algorithms involves decisions, such as how traces are represented, that can lead to better results. In this paper, we compare four vector space models for trace clustering, using them with an agglomerative clustering algorithm in synthetic and real-world event logs. Our analyses suggest the embeddings-based vector space model can properly handle trace clustering in unstructured processes.
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Bahmani, Amir, and Frank Mueller. "Chameleon: Online Clustering of MPI Program Traces." In 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2018. http://dx.doi.org/10.1109/ipdps.2018.00119.
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Cunha, Mariana, Ricardo Mendes, and Joao P. Vilela. "Clustering Geo-Indistinguishability for Privacy of Continuous Location Traces." In 2019 4th International Conference on Computing, Communications and Security (ICCCS). IEEE, 2019. http://dx.doi.org/10.1109/cccs.2019.8888111.
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Wang*, Wanli, Wuyang Yang, Xinjian Wei, and Xin He. "Abnormal traces identification method based on fuzzy clustering analysis." In SEG Technical Program Expanded Abstracts 2015. Society of Exploration Geophysicists, 2015. http://dx.doi.org/10.1190/segam2015-5801583.1.
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Wang, Jing, Xiaoping Rui, Xianfeng Song, Chaoling Wang, Lingli Tang, Chuanrong Li, and Venkatesh Raghvan. "A weighted clustering algorithm for clarifying vehicle GPS traces." In IGARSS 2011 - 2011 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2011. http://dx.doi.org/10.1109/igarss.2011.6049834.
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El Mandouh, Eman, and Amr G. Wassal. "Accelerating the debugging of FV traces using K-means clustering techniques." In 2016 11th International Design & Test Symposium (IDT). IEEE, 2016. http://dx.doi.org/10.1109/idt.2016.7843055.
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Tamagnan, Frédéric, Fabrice Bouquet, Alexandre Vernotte, and Bruno Legeard. "Regression Test Generation by Usage Coverage Driven Clustering on User Traces." In 2023 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). IEEE, 2023. http://dx.doi.org/10.1109/icstw58534.2023.00026.
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Meng, Fanzhi, Chunrui Zhang, and Guo Wu. "Protocol reverse based on hierarchical clustering and probability alignment from network traces." In 2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA). IEEE, 2018. http://dx.doi.org/10.1109/icbda.2018.8367724.
Full textReports on the topic "Traces clustering"
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Wang, Chih-Hao, and Na Chen. Do Multi-Use-Path Accessibility and Clustering Effect Play a Role in Residents' Choice of Walking and Cycling? Mineta Transportation Institute, June 2021. http://dx.doi.org/10.31979/mti.2021.2011.
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The transportation studies literature recognizes the relationship between accessibility and active travel. However, there is limited research on the specific impact of walking and cycling accessibility to multi-use paths on active travel behavior. Combined with the culture of automobile dependency in the US, this knowledge gap has been making it difficult for policy-makers to encourage walking and cycling mode choices, highlighting the need to promote a walking and cycling culture in cities. In this case, a clustering effect (“you bike, I bike”) can be used as leverage to initiate such a trend. This project contributes to the literature as one of the few published research projects that considers all typical categories of explanatory variables (individual and household socioeconomics, local built environment features, and travel and residential choice attitudes) as well as two new variables (accessibility to multi-use paths calculated by ArcGIS and a clustering effect represented by spatial autocorrelation) at two levels (level 1: binary choice of cycling/waking; level 2: cycling/walking time if yes at level 1) to better understand active travel demand. We use data from the 2012 Utah Travel Survey. At the first level, we use a spatial probit model to identify whether and why Salt Lake City residents walked or cycled. The second level is the development of a spatial autoregressive model for walkers and cyclists to examine what factors affect their travel time when using walking or cycling modes. The results from both levels, obtained while controlling for individual, attitudinal, and built-environment variables, show that accessibility to multi-use paths and a clustering effect (spatial autocorrelation) influence active travel behavior in different ways. Specifically, a cyclist is likely to cycle more when seeing more cyclists around. These findings provide analytical evidence to decision-makers for efficiently evaluating and deciding between plans and policies to enhance active transportation based on the two modeling approaches to assessing travel behavior described above.
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Russo, Margherita, Fabrizio Alboni, Jorge Carreto Sanginés, Manlio De Domenico, Giuseppe Mangioni, Simone Righi, and Annamaria Simonazzi. The Changing Shape of the World Automobile Industry: A Multilayer Network Analysis of International Trade in Components and Parts. Institute for New Economic Thinking Working Paper Series, January 2022. http://dx.doi.org/10.36687/inetwp173.
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In 2018, after 25 years of the North America Trade Agreement (NAFTA), the United States requested new rules which, among other requirements, increased the regional con-tent in the production of automotive components and parts traded between the three part-ner countries, United States, Canada and Mexico. Signed by all three countries, the new trade agreement, USMCA, is to go into force in 2022. Nonetheless, after the 2020 Presi-dential election, the new treaty's future is under discussion, and its impact on the automo-tive industry is not entirely defined. Another significant shift in this industry – the acceler-ated rise of electric vehicles – also occurred in 2020: while the COVID-19 pandemic largely halted most plants in the automotive value chain all over the world, at the reopen-ing, the tide is now running against internal combustion engine vehicles, at least in the an-nouncements and in some large investments planned in Europe, Asia and the US. The definition of the pre-pandemic situation is a very helpful starting point for the analysis of the possible repercussions of the technological and geo-political transition, which has been accelerated by the epidemic, on geographical clusters and sectorial special-isations of the main regions and countries. This paper analyses the trade networks emerg-ing in the past 25 years in a new analytical framework. In the economic literature on inter-national trade, the study of the automotive global value chains has been addressed by us-ing network analysis, focusing on the centrality of geographical regions and countries while largely overlooking the contribution of countries' bilateral trading in components and parts as structuring forces of the subnetwork of countries and their specific position in the overall trade network. The paper focuses on such subnetworks as meso-level structures emerging in trade network over the last 25 years. Using the Infomap multilayer clustering algorithm, we are able to identify clusters of countries and their specific trades in the automotive internation-al trade network and to highlight the relative importance of each cluster, the interconnec-tions between them, and the contribution of countries and of components and parts in the clusters. We draw the data from the UN Comtrade database of directed export and import flows of 30 automotive components and parts among 42 countries (accounting for 98% of world trade flows of those items). The paper highlights the changes that occurred over 25 years in the geography of the trade relations, with particular with regard to denser and more hierarchical network gener-ated by Germany’s trade relations within EU countries and by the US preferential trade agreements with Canada and Mexico, and the upsurge of China. With a similar overall va-riety of traded components and parts within the main clusters (dominated respectively by Germany, US and Japan-China), the Infomap multilayer analysis singles out which com-ponents and parts determined the relative positions of countries in the various clusters and the changes over time in the relative positions of countries and their specialisations in mul-tilateral trades. Connections between clusters increase over time, while the relative im-portance of the main clusters and of some individual countries change significantly. The focus on US and Mexico and on Germany and Central Eastern European countries (Czech Republic, Hungary, Poland, Slovakia) will drive the comparative analysis.
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Paynter, Robin A., Celia Fiordalisi, Elizabeth Stoeger, Eileen Erinoff, Robin Featherstone, Christiane Voisin, and Gaelen P. Adam. A Prospective Comparison of Evidence Synthesis Search Strategies Developed With and Without Text-Mining Tools. Agency for Healthcare Research and Quality (AHRQ), March 2021. http://dx.doi.org/10.23970/ahrqepcmethodsprospectivecomparison.
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Background: In an era of explosive growth in biomedical evidence, improving systematic review (SR) search processes is increasingly critical. Text-mining tools (TMTs) are a potentially powerful resource to improve and streamline search strategy development. Two types of TMTs are especially of interest to searchers: word frequency (useful for identifying most used keyword terms, e.g., PubReminer) and clustering (visualizing common themes, e.g., Carrot2). Objectives: The objectives of this study were to compare the benefits and trade-offs of searches with and without the use of TMTs for evidence synthesis products in real world settings. Specific questions included: (1) Do TMTs decrease the time spent developing search strategies? (2) How do TMTs affect the sensitivity and yield of searches? (3) Do TMTs identify groups of records that can be safely excluded in the search evaluation step? (4) Does the complexity of a systematic review topic affect TMT performance? In addition to quantitative data, we collected librarians' comments on their experiences using TMTs to explore when and how these new tools may be useful in systematic review search¬¬ creation. Methods: In this prospective comparative study, we included seven SR projects, and classified them into simple or complex topics. The project librarian used conventional “usual practice” (UP) methods to create the MEDLINE search strategy, while a paired TMT librarian simultaneously and independently created a search strategy using a variety of TMTs. TMT librarians could choose one or more freely available TMTs per category from a pre-selected list in each of three categories: (1) keyword/phrase tools: AntConc, PubReMiner; (2) subject term tools: MeSH on Demand, PubReMiner, Yale MeSH Analyzer; and (3) strategy evaluation tools: Carrot2, VOSviewer. We collected results from both MEDLINE searches (with and without TMTs), coded every citation’s origin (UP or TMT respectively), deduplicated them, and then sent the citation library to the review team for screening. When the draft report was submitted, we used the final list of included citations to calculate the sensitivity, precision, and number-needed-to-read for each search (with and without TMTs). Separately, we tracked the time spent on various aspects of search creation by each librarian. Simple and complex topics were analyzed separately to provide insight into whether TMTs could be more useful for one type of topic or another. Results: Across all reviews, UP searches seemed to perform better than TMT, but because of the small sample size, none of these differences was statistically significant. UP searches were slightly more sensitive (92% [95% confidence intervals (CI) 85–99%]) than TMT searches (84.9% [95% CI 74.4–95.4%]). The mean number-needed-to-read was 83 (SD 34) for UP and 90 (SD 68) for TMT. Keyword and subject term development using TMTs generally took less time than those developed using UP alone. The average total time was 12 hours (SD 8) to create a complete search strategy by UP librarians, and 5 hours (SD 2) for the TMT librarians. TMTs neither affected search evaluation time nor improved identification of exclusion concepts (irrelevant records) that can be safely removed from the search set. Conclusion: Across all reviews but one, TMT searches were less sensitive than UP searches. For simple SR topics (i.e., single indication–single drug), TMT searches were slightly less sensitive, but reduced time spent in search design. For complex SR topics (e.g., multicomponent interventions), TMT searches were less sensitive than UP searches; nevertheless, in complex reviews, they identified unique eligible citations not found by the UP searches. TMT searches also reduced time spent in search strategy development. For all evidence synthesis types, TMT searches may be more efficient in reviews where comprehensiveness is not paramount, or as an adjunct to UP for evidence syntheses, because they can identify unique includable citations. If TMTs were easier to learn and use, their utility would be increased.