Parallelization of implicit finite difference schemes in computational fluid dynamics

Implicit finite difference schemes are often the preferred numerical schemes in computational fluid dynamics, requiring less stringent stability bounds than the explicit schemes. Each iteration in an implicit scheme involves global data dependencies in the form of second and higher order recurrences. Efficient parallel implementations of such iterative methods are considerably more difficult and non-intuitive. The parallelization of the implicit schemes that are used for solving the Euler and the thin layer Navier-Stokes equations and that require inversions of large linear systems in the form of block tri-diagonal and/or block penta-diagonal matrices is discussed. Three-dimensional cases are emphasized and schemes that minimize the total execution time are presented. Partitioning and scheduling schemes for alleviating the effects of the global data dependencies are described. An analysis of the communication and the computation aspects of these methods is presented. The effect of the boundary conditions on the parallel schemes is also discussed

Divergence at low bias and down-mixing of the current noise in a diffusive superconductor-normal metal-superconductor junction

We present current noise measurements in a long diffusive superconductor-normal-metal-superconductor junction in the low voltage regime, in which transport can be partially described in terms of coherent multiple Andreev reflections. We show that, when decreasing voltage, the current noise exhibits a strong divergence together with a broad peak. We ascribe this peak to the mixing between the ac- Josephson current and the noise of the junction itself. We show that the junction noise corresponds to the thermal noise of a nonlinear resistor 4kBT=R with R V = I V and no adjustable parameters

Measure-Based Inconsistency-Tolerant Maintenance of Database Integrity

[EN] To maintain integrity, constraint violations should be prevented or repaired. However, it may not be feasible to avoid inconsistency, or to repair all violations at once. Based on an abstract concept of violation measures, updates and repairs can be checked for keeping inconsistency bounded, such that integrity violations are guaranteed to never get out of control. This measure-based approach goes beyond conventional methods that are not meant to be applied in the presence of inconsistency. It also generalizes recently introduced concepts of inconsistency-tolerant integrity maintenance.Partially supported by FEDER and the Spanish grants TIN2009-14460-C03 and TIN2010-17139Decker, H. (2013). Measure-Based Inconsistency-Tolerant Maintenance of Database Integrity. Lecture Notes in Computer Science. 7693:149-173. https://doi.org/10.1007/978-3-642-36008-4_7S1491737693Abiteboul, S., Hull, R., Vianu, V.: Foundations of Databases. Addison-Wesley (1995)Abiteboul, S., Vianu, V.: A transaction-based approach to relational database specification. JACM 36(4), 758–789 (1989)Afrati, F., Kolaitis, P.: Repair checking in inconsistent databases: algorithms and complexity. In: 12th ICDT, pp. 31–41. ACM Press (2009)Arenas, M., Bertossi, L., Chomicki, J.: Consistent query answers in inconsistent databases. In: PODS 1999, pp. 68–79. ACM Press (1999)Arieli, O., Denecker, M., Bruynooghe, M.: Distance semantics for database repair. Ann. Math. Artif. Intell. 50, 389–415 (2007)Arni-Bloch, N., Ralyté, J., Léonard, M.: Service–Driven Information Systems Evolution: Handling Integrity Constraints Consistency. In: Persson, A., Stirna, J. (eds.) PoEM 2009. LNBIP, vol. 39, pp. 191–206. Springer, Heidelberg (2009)Bauer, H.: Maß- und Integrationstheorie, 2. Auflage. De Gruyter (1992)Besnard, P., Hunter, A.: Quasi-Classical Logic: Non-Trivializable Classical Reasoning from Inconsistent Information. In: Froidevaux, C., Kohlas, J. (eds.) ECSQARU 1995. LNCS, vol. 946, pp. 44–51. Springer, Heidelberg (1995)Bohanon, P., Fan, W., Flaster, M., Rastogi, R.: A Cost-Based Model and Effective Heuristic for Repairing Constraints by Value Modification. In: Proc. SIGMOD 2005, pp. 143–154. ACM Press (2005)Ceri, S., Cochrane, R., Widom, J.: Practical Applications of Triggers and Constraints: Success and Lingering Issues. In: Proc. 26th VLDB, pp. 254–262. Morgan Kaufmann (2000)Chakravarthy, U., Grant, J., Minker, J.: Logic-based Approach to Semantic Query Optimization. Transactions on Database Systems 15(2), 162–207 (1990)Chomicki, J.: Consistent Query Answering: Five Easy Pieces. In: Schwentick, T., Suciu, D. (eds.) ICDT 2007. LNCS, vol. 4353, pp. 1–17. Springer, Heidelberg (2006)Christiansen, H., Martinenghi, D.: On simplification of database integrity constraints. Fundamenta Informaticae 71(4), 371–417 (2006)Clark, K.: Negation as Failure. In: Gallaire, H., Minker, J. (eds.) Logic and Data Bases, pp. 293–322. Plenum Press (1978)Curino, C., Moon, H., Deutsch, A., Zaniolo, C.: Update Rewriting and Integrity Constraint Maintenance in a Schema Evolution Support System: PRISM++. PVLDB 4, 117–128 (2010)Dawson, J.: The compactness of first-order logic: From Gödel to Lindström. History and Philosophy of Logic 14(1), 15–37 (1993)Decker, H.: The Range Form of Databases and Queries or: How to Avoid Floundering. In: Proc. 5th ÖGAI. Informatik-Fachberichte, vol. 208, pp. 114–123. Springer (1989)Decker, H.: Drawing Updates From Derivations. In: Kanellakis, P.C., Abiteboul, S. (eds.) ICDT 1990. LNCS, vol. 470, pp. 437–451. Springer, Heidelberg (1990)Decker, H.: Extending Inconsistency-Tolerant Integrity Checking by Semantic Query Optimization. In: Bhowmick, S.S., Küng, J., Wagner, R. (eds.) DEXA 2008. LNCS, vol. 5181, pp. 89–96. Springer, Heidelberg (2008)Decker, H.: Answers That Have Integrity. In: Schewe, K.-D., Thalheim, B. (eds.) SDKB 2010. LNCS, vol. 6834, pp. 54–72. Springer, Heidelberg (2011)Decker, H.: Causes of the Violation of Integrity Constraints for Supporting the Quality of Databases. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2011, Part V. LNCS, vol. 6786, pp. 283–292. Springer, Heidelberg (2011)Decker, H.: Inconsistency-tolerant Integrity Checking based on Inconsistency Metrics. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds.) KES 2011, Part II. LNCS, vol. 6882, pp. 548–558. Springer, Heidelberg (2011)Decker, H.: Partial Repairs that Tolerate Inconsistency. In: Eder, J., Bielikova, M., Tjoa, A.M. (eds.) ADBIS 2011. LNCS, vol. 6909, pp. 389–400. Springer, Heidelberg (2011)Decker, H.: Consistent Explanations of Answers to Queries in Inconsistent Knowledge Bases. In: Roth-Berghofer, T., Tintarev, N., Leake, D. (eds.) Explanation-aware Computing, Proc. IJCAI 2011 Workshop ExaCt 2011, pp. 71–80 (2011), http://exact2011.workshop.hm/index.phpDecker, H., Martinenghi, D.: Classifying integrity checking methods with regard to inconsistency tolerance. In: Proc. PPDP 2008, pp. 195–204. ACM Press (2008)Decker, H., Martinenghi, D.: Modeling, Measuring and Monitoring the Quality of Information. In: Heuser, C.A., Pernul, G. (eds.) ER 2009. LNCS, vol. 5833, pp. 212–221. Springer, Heidelberg (2009)Decker, H., Martinenghi, D.: Inconsistency-tolerant Integrity Checking. IEEE TKDE 23(2), 218–234 (2011)Decker, H., Muñoz-Escoí, F.D.: Revisiting and Improving a Result on Integrity Preservation by Concurrent Transactions. In: Meersman, R., Dillon, T., Herrero, P. (eds.) OTM 2010 Workshops. LNCS, vol. 6428, pp. 297–306. Springer, Heidelberg (2010)Dung, P., Kowalski, R., Toni, F.: Dialectic Proof Procedures for Assumption-based Admissible Argumentation. Artificial Intelligence 170(2), 114–159 (2006)Ebbinghaus, H.-D., Flum, J.: Finite Model Theory, 2nd edn. Springer (2006)Embury, S., Brandt, S., Robinson, J., Sutherland, I., Bisby, F., Gray, A., Jones, A., White, R.: Adapting integrity enforcement techniques for data reconciliation. Information Systems 26, 657–689 (2001)Enderton, H.: A Mathematical Introduction to Logic, 2nd edn. Academic Press (2001)Eiter, T., Fink, M., Greco, G., Lembo, D.: Repair localization for query answering from inconsistent databases. ACM TODS 33(2), article 10 (2008)Furfaro, F., Greco, S., Molinaro, C.: A three-valued semantics for querying and repairing inconsistent databases. Ann. Math. Artif. Intell. 51(2-4), 167–193 (2007)Grant, J., Hunter, A.: Measuring the Good and the Bad in Inconsistent Information. In: Proc. 22nd IJCAI, pp. 2632–2637 (2011)Greco, G., Greco, S., Zumpano, E.: A logical framework for querying and repairing inconsistent databases. IEEE TKDE 15(6), 1389–1408 (2003)Guessoum, A., Lloyd, J.: Updating knowledge bases. New Generation Computing 8(1), 71–89 (1990)Guessoum, A., Lloyd, J.: Updating knowledge bases II. New Generation Computing 10(1), 73–100 (1991)Gupta, A., Sagiv, Y., Ullman, J., Widom, J.: Constraint checking with partial information. In: Proc. PODS 1994, pp. 45–55. ACM Press (1994)Hunter, A.: Measuring Inconsistency in Knowledge via Quasi-Classical Models. In: Proc. 18th AAAI &14th IAAI, pp. 68–73 (2002)Hunter, A., Konieczny, S.: Approaches to Measuring Inconsistent Information. In: Bertossi, L., Hunter, A., Schaub, T. (eds.) Inconsistency Tolerance. LNCS, vol. 3300, pp. 191–236. Springer, Heidelberg (2005)Hunter, A., Konieczny, S.: Measuring inconsistency through minimal inconsistent sets. In: Brewka, G., Lang, J. (eds.) Principles of Knowledge Representation and Reasoning (Proc. 11th KR), pp. 358–366. AAAI Press (2008)Hunter, A., Konieczny, S.: On the measure of conflicts: Shapley Inconsistency Values. Artificial Intelligence 174, 1007–1026 (2010)Kakas, A., Mancarella, P.: Database updates through abduction. In: Proc. 16th VLDB, pp. 650–661. Morgan Kaufmann (1990)Kakas, A., Kowalski, R., Toni, F.: The role of Abduction in Logic Programming. In: Gabbay, D., Hogger, C., Robinson, J.A. (eds.) Handbook of Logic in Artificial Intelligence and Logic Programming, vol. 5, pp. 235–324. Oxford University Press (1998)Lee, S.Y., Ling, T.W.: Further improvements on integrity constraint checking for stratifiable deductive databases. In: Proc. VLDB 1996, pp. 495–505. Morgan Kaufmann (1996)Lehrer, K.: Relevant Deduction and Minimally Inconsistent Sets. Journal of Philosophy 3(2,3), 153–165 (1973)Mu, K., Liu, W., Jin, Z., Bell, D.: A Syntax-based Approach to Measuring the Degree of Inconsistency for Belief Bases. J. Approx. Reasoning 52(7), 978–999 (2011)Lloyd, J., Sonenberg, L., Topor, R.: Integrity constraint checking in stratified databases. J. Logic Programming 4(4), 331–343 (1987)Lozinskii, E.: Resolving contradictions: A plausible semantics for inconsistent systems. J. Automated Reasoning 12(1), 1–31 (1994)Ma, Y., Qi, G., Hitzler, P.: Computing inconsistency measure based on paraconsistent semantics. J. Logic Computation 21(6), 1257–1281 (2011)Martinenghi, D., Christiansen, H.: Transaction Management with Integrity Checking. In: Andersen, K.V., Debenham, J., Wagner, R. (eds.) DEXA 2005. LNCS, vol. 3588, pp. 606–615. Springer, Heidelberg (2005)Martinenghi, D., Christiansen, H., Decker, H.: Integrity Checking and Maintenance in Relational and Deductive Databases and Beyond. In: Ma, Z. (ed.) Intelligent Databases: Technologies and Applications, pp. 238–285. IGI Global (2006)Martinez, M.V., Pugliese, A., Simari, G.I., Subrahmanian, V.S., Prade, H.: How Dirty Is Your Relational Database? An Axiomatic Approach. In: Mellouli, K. (ed.) ECSQARU 2007. LNCS (LNAI), vol. 4724, pp. 103–114. Springer, Heidelberg (2007)Meyer, J., Wieringa, R. (eds.): Deontic Logic in Computer Science. Wiley (1994)Nicolas, J.M.: Logic for improving integrity checking in relational data bases. Acta Informatica 18, 227–253 (1982)Plexousakis, D., Mylopoulos, J.: Accommodating Integrity Constraints During Database Design. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 495–513. Springer, Heidelberg (1996)Rahm, E., Do, H.: Data Cleaning: Problems and Current Approaches. Data Engineering Bulletin 23(4), 3–13 (2000)Sadri, F., Kowalski, R.: A theorem-proving approach to database integrity. In: Minker, J. (ed.) Foundations of Deductive Databases and Logic Programming, pp. 313–362. Morgan Kaufmann (1988)Thimm, M.: Measuring Inconsistency in Probabilistic Knowledge Bases. In: Proc. 25th UAI, pp. 530–537. AUAI Press (2009)Vardi, M.: On the integrity of databases with incomplete information. In: Proc. 5th PODS, pp. 252–266. ACM Press (1986)Wijsen, J.: Database repairing using updates. ACM Trans. Database Syst. 30(3), 722–768 (2005

Gang members are entangled in a web of violence that leads the gunman of today to become the victim of tomorrow

While the media often portrays a stark line between the victims of crime and offenders the reality is much more blurred. New research from David Pyrooz, Richard K. Moule, and Scott H. Decker find that this is especially the case for gang members who find that they are twice as likely to be both victims and offenders as non-gang members. They argue that gang membership is a large risk factor in this victim-offender overlap, as single acts of violence between gang members often lead to acts of retribution between gangs as a whole

Data consistency: toward a terminological clarification

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-21413-9_15Consistency is an inconsistency are ubiquitous term in data engineering. Its relevance to quality is obvious, since consistency is a commonplace dimension of data quality. However, connotations are vague or ambiguous. In this paper, we address semantic consistency, transaction consistency, replication consistency, eventual consistency and the new notion of partial consistency in databases. We characterize their distinguishing properties, and also address their differences, interactions and interdependencies. Partial consistency is an entry door to living with inconsistency, which is an ineludible necessity in the age of big data.Decker and F.D. Muñoz—supported by the Spanish MINECO grant TIN 2012-37719-C03-01.Decker, H.; Muñoz Escoí, FD.; Misra, S. (2015). Data consistency: toward a terminological clarification. En Computational Science and Its Applications -- ICCSA 2015: 15th International Conference, Banff, AB, Canada, June 22-25, 2015, Proceedings, Part V. Springer International Publishing. 206-220. https://doi.org/10.1007/978-3-319-21413-9_15S206220Abadi, D.: Consistency tradeoffs in modern distributed database system design: Cap is only part of the story. Computer 45(2), 37–42 (2012)Bailis, P. (2015). http://www.bailis.org/blog/Bailis, P., Ghodsi, A.: Eventual consistency today: limitations, extensions, and beyond. ACM Queue, 11(3) (2013)Balegas, V., Duarte, S., Ferreira, C., Rodrigues, R., Preguica, N., Najafzadeh, M., Shapiro, M.: Putting consistency back into eventual consistency. In: 10th EuroSys. ACM (2015). http://dl.acm.org/citation.cfm?doid=2741948.2741972Beeri, C., Bernstein, P., Goodman, N.: A sophisticate’s introduction to database normalization theory. In: VLDB, pp. 113–124 (1978)Berenson, H., Bernstein, P., Gray, J., Melton, J., O’Neil, E., O’Neil, P.: A critique of ansi sql isolation levels. SIGMoD Record 24(2), 1–10 (1995)Bermbach, D., Tai, S.: Eventual consistency: how soon is eventual? In: 6th MW4SOC. ACM (2011)Bernabé-Gisbert, J., Muñoz-Escoí, F.: Supporting multiple isolation levels in replicated environments. Data & Knowledge Engineering 7980, 1–16 (2012)Bernstein, P., Das, S.. Rethinking eventual consistency. In: SIGMOD 2013, pp. 923–928. ACM (2013)Bernstein, P., Hadzilacos, V., Goodman, N.: Concurrency Control and Recovery in Database Systems. Addison-Wesley (1987)Bertossi, L., Hunter, A., Schaub, T.: Inconsistency Tolerance. In: Bertossi, L., Hunter, A., Schaub, T. (eds.) Inconsistency Tolerance. LNCS, vol. 3300, pp. 1–14. Springer, Heidelberg (2005)Bobenrieth, A.: Inconsistencias por qué no? Un estudio filosófico sobre la lógica paraconsistente. Premios Nacionales Colcultura. Tercer Mundo Editores. Magister Thesis, Universidad de los Andes, Santafé de Bogotá, Columbia (1995)Bosneag, A.-M., Brockmeyer, M.: A formal model for eventual consistency semantics. In: PDCS 2002, pp. 204–209. IASTED (2001)Browne, J.: Brewer’s cap theorem (2009). http://www.julianbrowne.com/article/viewer/brewers-cap-theoremCong, G., Fan, W., Geerts, F., Jia, X., Ma, S.: Improving data quality: consistency and accuracy. In: Proc. 33rd VLDB, pp. 315–326. ACM (2007)Dechter, R., van Beek, P.: Local and global relational consistency. Theor. Comput. Sci. 173(1), 283–308 (1997)Decker, H.: Translating advanced integrity checking technology to SQL. In: Doorn, J., Rivero, L. (eds.) Database integrity: challenges and solutions, pp. 203–249. Idea Group (2002)Decker, H.: Historical and computational aspects of paraconsistency in view of the logic foundation of databases. In: Bertossi, L., Katona, G.O.H., Schewe, K.-D., Thalheim, B. (eds.) Semantics in Databases 2001. LNCS, vol. 2582, pp. 63–81. Springer, Heidelberg (2003)Decker, H.: Answers that have integrity. In: Schewe, K.-D., Thalheim, B. (eds.) SDKB 2010. LNCS, vol. 6834, pp. 54–72. Springer, Heidelberg (2011)Decker, H.: New measures for maintaining the quality of databases. In: Murgante, B., Gervasi, O., Misra, S., Nedjah, N., Rocha, A.M.A.C., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2012, Part IV. LNCS, vol. 7336, pp. 170–185. Springer, Heidelberg (2012)Decker, H.: A pragmatic approach to model, measure and maintain the quality of information in databases (2012). www.iti.upv.es/~hendrik/papers/ahrc-workshop_quality-of-data.pdf , www.iti.upv.es/~hendrik/papers/ahrc-workshop_quality-of-data_comments.pdf . Slides and comments presented at the Workshop on Information Quality. Univ, Hertfordshire, UKDecker, H.: Answers that have quality. In: Murgante, B., Misra, S., Carlini, M., Torre, C.M., Nguyen, H.-Q., Taniar, D., Apduhan, B.O., Gervasi, O. (eds.) ICCSA 2013, Part II. LNCS, vol. 7972, pp. 543–558. Springer, Heidelberg (2013)Decker, H.: Measure-based inconsistency-tolerant maintenance of database integrity. In: Schewe, K.-D., Thalheim, B. (eds.) SDKB 2013. LNCS, vol. 7693, pp. 149–173. Springer, Heidelberg (2013)Decker, H., Martinenghi, D.: Inconsistency-tolerant integrity checking. IEEE Transactions of Knowledge and Data Engineering 23(2), 218–234 (2011)Decker, H., Muñoz-Escoí, F.D.: Revisiting and improving a result on integrity preservation by concurrent transactions. In: Meersman, R., Dillon, T., Herrero, P. (eds.) OTM 2010. LNCS, vol. 6428, pp. 297–306. Springer, Heidelberg (2010)Dong, X.L., Berti-Equille, L., Srivastava, D.: Data fusion: resolving conflicts from multiple sources (2015). http://arxiv.org/abs/1503.00310Eswaran, K., Gray, J., Lorie, R., Traiger, I.: The notions of consistency and predicate locks in a database system. CACM 19(11), 624–633 (1976)Muñoz-Escoí, F.D., Ruiz-Fuertes, M.I., Decker, H., Armendáriz-Íñigo, J.E., de Mendívil, J.R.G.: Extending middleware protocols for database replication with integrity support. In: Meersman, R., Tari, Z. (eds.) OTM 2008, Part I. LNCS, vol. 5331, pp. 607–624. Springer, Heidelberg (2008)Fekete, A.: Consistency models for replicated data. In: Encyclopedia of Database Systems, pp. 450–451. Springer (2009)Fekete, A., Gupta, D., Lynch, V., Luchangco, N., Shvartsman, A.: Eventually-serializable data services. In: 15th PoDC, pp. 300–309. ACM (1996)Gilbert, S., Lynch, N.: Brewer’s conjecture and the feasibility of consistent, available, partition-tolerant web services. SIGACT News 33(2), 51–59 (2002)Golab, W., Rahman, M., Auyoung, A., Keeton, K., Li, X.: Eventually consistent: Not what you were expecting? ACM Queue, 12(1) (2014)Grant, J., Hunter, A.: Measuring inconsistency in knowledgebases. Journal of Intelligent Information Systems 27(2), 159–184 (2006)Gray, J., Lorie, R., Putzolu, G., Traiger, I.: Granularity of locks and degrees of consistency in a shared data base. In: Nijssen, G. (ed.) Modelling in Data Base Management Systems. North Holland (1976)Haerder, T., Reuter, A.: Principles of transaction-oriented database recovery. Computing Surveys 15(4), 287–317 (1983)Herlihy, M., Wing, J.: Linearizability: a correctness condition for concurrent objects. TOPLAS 12(3), 463–492 (1990)R. Ho. Design pattern for eventual consistency (2009). http://horicky.blogspot.com.es/2009/01/design-pattern-for-eventual-consistency.htmlIkeda, R., Park, H., Widom, J.: Provenance for generalized map and reduce workflows. In: CIDR (2011)Kempster, T., Stirling, C., Thanisch, P.: Diluting acid. SIGMoD Record 28(4), 17–23 (1999)Li, X., Dong, X.L., Meng, W., Srivastava, D.: Truth finding on the deep web: Is the problem solved? VLDB Endowment 6(2), 97–108 (2012)Lloyd, W., Freedman, M., Kaminsky, M., Andersen, D.: Don’t settle for eventual: scalable causal consistency for wide-area storage with cops. In: 23rd SOPS, pp. 401–416 (2011)Lomet, D.: Transactions: from local atomicity to atomicity in the cloud. In: Jones, C.B., Lloyd, J.L. (eds.) Dependable and Historic Computing. LNCS, vol. 6875, pp. 38–52. Springer, Heidelberg (2011)Monge, P., Contractor, N.: Theory of Communication Networks. Oxford University Press (2003)Nicolas, J.-M.: Logic for improving integrity checking in relational data bases. Acta Informatica 18, 227–253 (1982)Muñoz-Escoí, F.D., Irún, L., H. Decker: Database replication protocols. In: Encyclopedia of Database Technologies and Applications, pp. 153–157. IGI Global (2005)Oracle: Constraints. http://docs.oracle.com/cd/B19306_01/server.102/b14223/constra.htm (May 1, 2015)Ouzzani, M., Medjahed, B., Elmagarmid, A.: Correctness criteria beyond serializability. In: Encyclopedia of Database Systems, pp. 501–506. Springer (2009)Rosenkrantz, D., Stearns, R., Lewis, P.: Consistency and serializability in concurrent datanbase systems. SIAM J. Comput. 13(3), 508–530 (1984)Saito, Y., Shapiro, M.: Optimistic replication. JACM 37(1), 42–81 (2005)Sandhu, R.: On five definitions of data integrity. In: Proc. IFIP WG11.3 Workshop on Database Security, pp. 257–267. North-Holland (1994)Simmons, G.: Contemporary Cryptology: The Science of Information Integrity. IEEE Press (1992)Sivathanu, G., Wright, C., Zadok, E.: Ensuring data integrity in storage: techniques and applications. In: Proc. 12th Conf. on Computer and Communications Security, p. 26. ACM (2005)Svanks, M.: Integrity analysis: Methods for automating data quality assurance. Information and Software Technology 30(10), 595–605 (1988)Technet, M.: Data integrity. https://technet.microsoft.com/en-us/library/aa933058 (May 1, 2015)Terry, D.: Replicated data consistency explained through baseball. Technical report, Microsoft. MSR Technical Report (2011)Traiger, I., Gray, J., Galtieri, C., Lindsay, B.: Transactions and consistency in distributed database systems. ACM Trans. Database Syst. 7(3), 323–342 (1982)Vidyasankar, K.: Serializability. In: Encyclopedia of Database Systems, pp. 2626–2632. Springer (2009)Vogels, W.: Eventually consistent (2007). http://www.allthingsdistributed.com/2007/12/eventually_consistent.html . Other versions in ACM Queue 6(6), 14–19. http://queue.acm.org/detail.cfm?id=1466448 (2008) and CACM 52(1), 40–44 (2009)Wikipedia: Consistency model. http://en.wikipedia.org/wiki/Consistency_model (May 1, 2015)Wikipedia: Data integrity. http://en.wikipedia.org/wiki/Data_integrity (May 1, 2015)Wikipedia: Data quality. http://en.wikipedia.org/wiki/Data_quality (May 1, 2015)Yin, X., Han, J., Yu, P.: Truth discovery with multiple conflicting information providers on the web. IEEE Transactions of Knowledge and Data Engineering 20(6), 796–808 (2008)Young, G.: Quick thoughts on eventual consistency (2010). http://codebetter.com/gregyoung/2010/04/14/quick-thoughts-on-eventual-consistency/ (May 1, 2015

Answers that Have Integrity

[EN] Answers to queries in possibly inconsistent databases may not have integrity. We formalize ‘has integrity’ on the basis of a definition of ‘causes’. A cause of an answer is a minimal excerpt of the database that explains why the answer has been given. An answer has integrity if one of its causes does not overlap with any cause of integrity violation.Supported by FEDER and the Spanish grants TIN2009-14460-C03, TIN2010-17139.Decker, H. (2011). Answers that Have Integrity. Lecture Notes in Computer Science. 6834:54-72. https://doi.org/10.1007/978-3-642-23441-5S5472683

Prevalence of \u3ci\u3eEimeria\u3c/i\u3e (Apicomplexa: Eimeriidae) in Reintroduced Gunnison\u27s Prairie Dogs (\u3ci\u3eCynomys gunnisoni\u3c/i\u3e)

Fecal samples from 54 Gunnison’s prairie dogs (Cynomys gunnisoni) from Albuquerque, NM were analyzed for the presence of coccidia and all were positive. They were then relocated to an abandoned prairie dog town on the Sevilleta Long Term Ecological Research (LTER) site. Six Eimeria species, E. callospermophili, E. cynomysis, E. pseudospermophili (new host record), E. spermophili, E. ludoviciani and E. vilasi (new host record) were found in Albuquerque animals, but only two species, E. callospermophili and E. vilasi were present in relocated hosts. A significant (P \u3c 0.05) reduction was seen in the prevalence of E. vilasi (72% vs. 13%) and in the prevalence of infections (P \u3c 0.05) with two or more Eimeria species (39% vs. 4%) in pre- and postrelocation animals. To assess the impact of the introduction of C. gunnisoni on the resident rodent population, feces were collected from 6 species of rodents. Five Eimeria species, E. arizonensis (Reithrodontomys), E. chobotari (Dipodomys, Perognathus), E. liomysis (Dipodomys), E. mohavensis (Dipodomys) and E. reedi (Perognathus) were found. We found no evidence of coccidia transfer among introduced and resident rodent species

Closed orbit correction at synchrotrons for symmetric and near-symmetric lattices

This contribution compiles the benefits of lattice symmetry in the context of closed orbit correction. A symmetric arrangement of BPMs and correctors results in structured orbit response matrices of Circulant or block Circulant type. These forms of matrices provide favorable properties in terms of computational complexity, information compression and interpretation of mathematical vector spaces of BPMs and correctors. For broken symmetries, a nearest-Circulant approximation is introduced and the practical advantages of symmetry exploitation are demonstrated with the help of simulations and experiments in the context of FAIR synchrotrons