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

Iron Behaving Badly: Inappropriate Iron Chelation as a Major Contributor to the Aetiology of Vascular and Other Progressive Inflammatory and Degenerative Diseases

The production of peroxide and superoxide is an inevitable consequence of aerobic metabolism, and while these particular "reactive oxygen species" (ROSs) can exhibit a number of biological effects, they are not of themselves excessively reactive and thus they are not especially damaging at physiological concentrations. However, their reactions with poorly liganded iron species can lead to the catalytic production of the very reactive and dangerous hydroxyl radical, which is exceptionally damaging, and a major cause of chronic inflammation. We review the considerable and wide-ranging evidence for the involvement of this combination of (su)peroxide and poorly liganded iron in a large number of physiological and indeed pathological processes and inflammatory disorders, especially those involving the progressive degradation of cellular and organismal performance. These diseases share a great many similarities and thus might be considered to have a common cause (i.e. iron-catalysed free radical and especially hydroxyl radical generation). The studies reviewed include those focused on a series of cardiovascular, metabolic and neurological diseases, where iron can be found at the sites of plaques and lesions, as well as studies showing the significance of iron to aging and longevity. The effective chelation of iron by natural or synthetic ligands is thus of major physiological (and potentially therapeutic) importance. As systems properties, we need to recognise that physiological observables have multiple molecular causes, and studying them in isolation leads to inconsistent patterns of apparent causality when it is the simultaneous combination of multiple factors that is responsible. This explains, for instance, the decidedly mixed effects of antioxidants that have been observed, etc...Comment: 159 pages, including 9 Figs and 2184 reference

SIRC, a Multiple Isolation Level Protocol for Middleware-based Data Replication

Abstract—One of the weaknesses of database replication protocols, compared to centralized DBMSs, is that they are unable to manage concurrent execution of transactions at different isolation levels. In the last years, some theoretical works related to this research line have appeared but none of them has proposed and implemented a real replication protocol with support to multiple isolation levels. This paper takes advantage of our MADIS middleware and one of its implemented Snapshot Isolation protocols to design and implement SIRC, a protocol that is able to execute concurrently both Generalized Snapshot Isolation (GSI) and Generalized Loose Read Committed (GLRC) transactions. We have also made a performance analysis to show how this kind of protocols can improve the system performance and decrease the transaction abortion rate in applications that do not require the strictest isolation level in every transaction. I

Providing read committed isolation level in non-blocking ROWA database replication protocols

Abstract. Total order ROWA strategies are widely used in replicated protocol design. Normally, these protocols provide higher isolation guarantees like Serialisable or Snapshot Isolation but, for some applications, a more permissive isolation level like Read Committed fits better. Some centralised database management systems provide Read Committed as a default isolation level but in replicated systems it is rare to find proposals and systems supporting it. In this paper we extend the notion of Read Committed to a replicated environment, giving the necessary theoretical background to construct Read Committed ROWA database replication protocols.

Abstract SIRC-Rep, a Multiple Isolation Level Protocol for Middleware-based Data Replication Architectures ∗

One of the weaknesses of replicated protocols, compared to centralized ones, is that they are unable to manage concurrent execution of transactions at different isolation levels. In the last years, some theoretical works related to this research line have appeared but none of them has proposed and implemented a real replication protocol with support to multiple isolation levels. This paper takes advantage of our MADIS middleware and one of its implemented Snapshot Isolation protocols to design and implement the SIRC-Rep, a protocol that is able to execute concurrently both Generalized Snapshot Isolation (SI) and Read Committed (RC) transactions. We have also made a performance analysis to show how this kind of protocols can improve the system performance and decrease the transaction abortion rate in applications that do not require the strictest isolation level in every transaction. ∗ This work has been partially supported by the Spanis

A Metaprotocol Outline for Database Replication Adaptability

Abstract. Database replication tasks are accomplished with the aid of consistency protocols. Commonly, proposed solutions use a single replication protocol providing just one isolation level. The main drawback of this approach is its lack of flexibility for changing scenarios –i.e. workloads, access patterns... – or heterogeneous client application requirements. This work proposes a metaprotocol for supporting several replication protocols which use different replication techniques or provide different isolation levels. With this metaprotocol, replication protocols can either work concurrently with the same data or be sequenced for adapting to changing environments. In this line, the use of a load monitor would enable the best choice for each transaction, selecting the most appropriate protocol according to the current system characteristics. This paper is focused on outlining this metaprotocol design, establishing the metadata set needed and the required interaction between the main database replication protocol families.