Jin D, Liu D, Yang B, Moreno CB, He D.  2011.  Ant colony optimization with Markov random walk for community detection in graphs. Advances in Knowledge Discovery and Data Mining. 6635:123–134. Abstract

Network clustering problem (NCP) is the problem associated to the detection of network community structures. Building on Markov random walks we address this problem with a new ant colony optimization strategy, named as ACOMRW, which improves prior results on the NCP problem and does not require knowledge of the number of communities present on a given network. The framework of ant colony optimization is taken as the basic framework in the ACOMRW algorithm. At each iteration, a Markov random walk model is taken as heuristic rule; all of the ants’ local solutions are aggregated to a global one through clustering ensemble, which then will be used to update a pheromone matrix. The strategy relies on the progressive strengthening of within-community links and the weakening of between-community links. Gradually this converges to a solution where the underlying community structure of the complex network will become clearly visible. The performance of algorithm ACOMRW was tested on a set of benchmark computer-generated networks, and as well on real-world network data sets. Experimental results confirm the validity and improvements met by this approach.

Moreno CB, Gonçalves N, José R.  2011.  Privacy preserving gate counting with collaborative bluetooth scanners. Workshops On the Move to Meaningful Internet Systems. 7046:534–543. Abstract

Due to its pervasiveness and communication capabilities, Bluetooth can be used as an infrastructure for several situated interaction and massive sensing scenarios. This paper shows how Bluetooth scanning can be used in gate counting scenarios, where the main goal is to provide an accurate count for the number of unique devices sighted. To this end, we present an analysis of several stochastic counting techniques that not only provide an accurate count for the number of unique devices, but o er privacy guarantees as well.

Ferreira JF, Mendes A, Cunha A, Moreno CB, Silva P, Barbosa LS, Oliveira JN.  2011.  Logic Training through Algorithmic Problem Solving. Tools for Teaching Logic. 6680:62-69. Abstractticttl11.pdf

Although much of mathematics is algorithmic in nature, the skills needed to formulate and solve algorithmic problems do not form an integral part of mathematics education. In particular, logic, which is central to algorithm development, is rarely taught explicitly at pre university level, under the justi cation that it is implicit in mathematics and therefore does not need to be taught as an independent topic. This paper argues in the opposite direction, describing a one week workshop done at the University of Minho, in Portugal, whose goal was to introduce to high-school students calculational principles and techniques of algorithmic problem solving supported by calculational logic. The work shop resorted to recreational problems to convey the principles and to software tools, the Alloy Analyzer and Netlogo, to animate models.

Sousa P, Preguiça N, Moreno CB.  2009.  Forby: providing groupware features relying on distributed file system event dissemination. Groupware: Design, Implementation, and Use. 5784(5784):158–173. Abstract

Abstract. Intensive research and development has been conducted in the design and creation of groupware systems for distributed users. While for some activities, these groupware tools are widely used, for other activities the impact in the groupware community has been smaller and can be improved. One reason for this fact is that the mostly common used applications do not support collaborative features and users are reluctant to change to a different application. In this paper we discuss how available file system mechanisms can help to address this problem. In this context, we present Forby, a system that allows to provide groupware features to distributed users by combining filesystem monitoring and distributed event dissemination. To demonstrate our solution, we present three systems that rely on Forby for providing groupware features to users running unmodified applications.

Almeida PS, Moreno CB, Fonte V.  2007.  Improving on version stamps. On the Move to Meaningful Internet Systems 2007: OTM 2007 Workshops. 4806:1025–1031. Abstract

Optimistic distributed systems often rely on version vectors or their variants in order to track updates on replicated objects. Some of these mechanisms rely on some form of global configuration or distributed naming protocol in order to assign unique identifiers to each replica. These approaches are incompatible with replica creation under arbitrary partitions, a typical operation mode in mobile or poorly connected environments. Other mechanisms assign unique identifiers relying on statistical correctness. In previous work we have introduced an update tracking mechanism that overcomes these limitations. This paper presents results from recent experimentation, that brought to surface a particular pattern of operation that results in an unforeseen, unlimited growth in space consumption. We also describe informally a new update tracking mechanism that does not exhibit this pathological growth while providing guaranteed unique identifiers for a dynamic number of replicas under arbitrary partitions and the same functionality of version vectors.

Enes V, Moreno CB, Almeida PS, Leitão J.  2017.  Borrowing an Identity for a Distributed Counter. PaPoC '17 Proceedings of the 3rd Workshop on the Principles and Practice of Consistency for Distributed Data. a5-enes.pdf
Younes G, Almeida PS, Moreno CB.  2017.  Compact Resettable Counters through Causal Stability. PaPoC '17 Proceedings of the 3rd Workshop on the Principles and Practice of Consistency for Distributed Data. a3-younes.pdf
Proença J, Moreno CB.  2017.  Quality-Aware Reactive Programming for the Internet of Things. 7th IPM International Conference on Fundamentals of Software Engineering. quarp.pdf
Moreno CB, Almeida PS, Lerche C.  2016.  The problem with embedded CRDT counters and a solution. PaPoC '16 Proceedings of the 2nd Workshop on the Principles and Practice of Consistency for Distributed Data. abstractcounterpapocfinal.pdf
Zawirski M, Moreno CB, Zawirski M, Preguiça N, Shapiro M.  2016.  Eventually Consistent Register Revisited. Proceeding PaPoC '16 Proceedings of the 2nd Workshop on the Principles and Practice of Consistency for Distributed Data. mvreg_papoc_camera.pdf
Almeida PS, Shoker A, Moreno CB.  2015.  Efficient State-based CRDTs by Delta-Mutation. In the Proceedings of the International Conference on NETworked sYStems}. 9466 Abstractdeltacrdt.pdf

CRDTs are distributed data types that make eventual consistency of a distributed object possible and non ad-hoc. Specifically, state-based CRDTs ensure convergence through disseminating the entire state, that may be large, and merging it to other replicas; whereas operation-based CRDTs disseminate operations (i.e., small states) assuming an exactly-once reliable dissemination layer. We introduce Delta State Conflict-Free Replicated Datatypes (δ-CRDT) that can achieve the best of both worlds: small messages with an incremental nature, as in operation-based CRDTs, disseminated over unreliable communication channels, as in traditional state-based CRDTs. This is achieved by defining δ-mutators to return a delta-state, typically with a much smaller size than the full state, that is joined to both: local and remote states. We introduce the δ-CRDT framework, and we explain it through establishing a correspondence to current state-based CRDTs. In addition, we present an anti-entropy algorithm that ensures causal consistency, and we introduce two δ-CRDT specifications of well-known replicated datatypes.

Almeida PS, Moreno CB, Fonte V, Gonçalves R.  2015.  Concise Server-Wide Causality Management for Eventually Consistent Data Stores. DAIS - IFIP International Conference on Distributed Applications and Interoperable Systems. Abstractglobal_logical_clocks.pdf

Large scale distributed data stores rely on optimistic replication to scale and remain highly available in the face of network partitions. Managing data without coordination results in eventually consistent data stores that allow for concurrent data updates. These systems often use anti-entropy mechanisms (like Merkle Trees) to detect and repair divergent data versions across nodes. However, in practice hash-based data structures are too expensive for large amounts of data and create too many false conflicts. Another aspect of eventual consistency is detecting write conflicts. Logical clocks are often used to track data causality, necessary to detect causally concurrent writes on the same key. However, there is a nonnegligible metadata overhead per key, which also keeps growing with time, proportional with the node churn rate. Another challenge is deleting keys while respecting causality: while the values can be deleted, perkey metadata cannot be permanently removed without coordination. We introduce a new causality management framework for eventually consistent data stores, that leverages node logical clocks (Bitmapped Version Vectors) and a new key logical clock (Dotted Causal Container) to provides advantages on multiple fronts: 1) a new efficient and lightweight anti-entropy mechanism; 2) greatly reduced per-key causality metadata size; 3) accurate key deletes without permanent metadata.

Moreno CB, Lima R, Miranda H.  2015.  Adaptive Broadcast Cancellation Query Mechanism for Unstructured Networks. 9th International Conference on Next Generation Mobile Applications, Services and Technologies. Abstract142629213215577.pdf

The availability of cheap embedded sensors in mobile devices boosted the emergence of unstructured networks using wireless technologies without centralised administration. However, a simple task such as collecting temperature needs a discovery service to find a thermometer. Usually, the resource discovery relies on flooding mechanisms, wasting energy and compromising system availability. On the other hand, energy efficient solutions based on broadcast cancellation mechanism have a significant impact on latency. The paper proposes ABC (Adaptive Broadcast Cancellation) a new algorithm that uses the knowledge acquired in the past to accelerate queries towards the resource. Each node listens to its neighbours and the acquired context is stored in a variation of bloom filters.

Lima R, Moreno CB, Miranda H.  2015.  FBL - Filtro Bloom Linear. Infórum 2015. Abstractlima15.pdf

As estruturas de dados que permitem o armazenamento de informação de forma probabilística (em particular, os filtros de Bloom) caracterizam-se por permitir a regulação do equilíbrio entre a eficiência na gestão do espaço de armazenamento e a precisão das respostas. Esta possibilidade tem motivado a sua utilização em cenários adversos, por exemplo em redes de sensores, onde os dispositivos apresentam recursos limitados (memória, cpu, energia). Este artigo apresenta um mecanismo de Filtro de Bloom Linear (FBL), que permite associar a cada um dos elementos uma probabilidade quantizada no intervalo real [0,1], ultrapassando as limitações impostas pela característica binária dos filtros de Bloom. Os resultados mostram que é possível parametrizar um FBL de modo a manter um erro aceitável em função da variação do número de bits usados na quantização e do número de funções de hash usadas na indexação. O artigo discute a aplicação dos FBLs em mecanismos de disseminação e descoberta de recursos em redes de sensores, mostrando como contribuem para manter uma dimensão constante das mensagens trocadas pelos sensores, independentemente da dimensão da rede.

Almeida PS, Moreno CB, Gonçalves R, Preguiça N, Fonte V.  2014.  Scalable and Accurate Causality Tracking for Eventually Consistent Stores. Distributed Applications and Interoperable Systems. 8460 Abstractdvvset-dais.pdf

In cloud computing environments, data storage systems often rely on optimistic replication to provide good performance and availability even in the presence of failures or network partitions. In this scenario, it is important to be able to accurately and efficiently identify updates executed concurrently. Current approaches to causality tracking in optimistic replication have problems with concurrent updates: they either (1) do not scale, as they require replicas to maintain information that grows linearly with the number of writes or unique clients; (2) lose information about causality, either by removing entries from client-id based version vectors or using server-id based version vectors, which cause false conflicts. We propose a new logical clock mechanism and a logical clock framework that together support a traditional key-value store API, while capturing causality in an accurate and scalable way, avoiding false conflicts. It maintains concise information per data replica, only linear on the number of replica servers, and allows data replicas to be compared and merged linear with the number of replica servers and versions.