eCMT-SCTP: Improving Performance of Multipath SCTP with Erasure Coding Over Lossy Links

Performance of transport protocols on lossy links is a well-researched topic, however there are only a few proposals making use of the opportunities of erasure coding within the multipath transport protocol context. In this paper, we investigate performance improvements of multipath CMT-SCTP with the novel integration of the on-the-fly erasure code within congestion control and reliability mechanisms. Our contributions include: integration of transport protocol and erasure codes with regards to congestion control; proposal for a variable retransmission delay parameter (aRTX) adjustment; performance evaluation of CMT-SCTP with erasure coding with simulations. We have implemented the explicit congestion notification (ECN) and erasure coding schemes in NS-2, evaluated and demonstrated results of improvement both for application goodput and decline of spurious retransmission. Our results show that we can achieve from 10% to 80% improvements in goodput under lossy network conditions without a significant penalty and minimal overhead due to the encoding-decoding process

Improved Handover Through Dual Connectivity in 5G mmWave Mobile Networks

The millimeter wave (mmWave) bands offer the possibility of orders of magnitude greater throughput for fifth generation (5G) cellular systems. However, since mmWave signals are highly susceptible to blockage, channel quality on any one mmWave link can be extremely intermittent. This paper implements a novel dual connectivity protocol that enables mobile user equipment (UE) devices to maintain physical layer connections to 4G and 5G cells simultaneously. A novel uplink control signaling system combined with a local coordinator enables rapid path switching in the event of failures on any one link. This paper provides the first comprehensive end-to-end evaluation of handover mechanisms in mmWave cellular systems. The simulation framework includes detailed measurement-based channel models to realistically capture spatial dynamics of blocking events, as well as the full details of MAC, RLC and transport protocols. Compared to conventional handover mechanisms, the study reveals significant benefits of the proposed method under several metrics.Comment: 16 pages, 13 figures, to appear on the 2017 IEEE JSAC Special Issue on Millimeter Wave Communications for Future Mobile Network

End-to-End Entity Resolution for Big Data: A Survey

One of the most important tasks for improving data quality and the reliability of data analytics results is Entity Resolution (ER). ER aims to identify different descriptions that refer to the same real-world entity, and remains a challenging problem. While previous works have studied specific aspects of ER (and mostly in traditional settings), in this survey, we provide for the first time an end-to-end view of modern ER workflows, and of the novel aspects of entity indexing and matching methods in order to cope with more than one of the Big Data characteristics simultaneously. We present the basic concepts, processing steps and execution strategies that have been proposed by different communities, i.e., database, semantic Web and machine learning, in order to cope with the loose structuredness, extreme diversity, high speed and large scale of entity descriptions used by real-world applications. Finally, we provide a synthetic discussion of the existing approaches, and conclude with a detailed presentation of open research directions

BLAST: a Loosely Schema-aware Meta-blocking Approach for Entity Resolution

Identifying records that refer to the same entity is a fundamental step for data integration. Since it is prohibitively expensive to compare every pair of records, blocking techniques are typically employed to reduce the complexity of this task. These techniques partition records into blocks and limit the comparison to records co-occurring in a block. Generally, to deal with highly heterogeneous and noisy data (e.g. semi-structured data of the Web), these techniques rely on redundancy to reduce the chance of missing matches. Meta-blocking is the task of restructuring blocks generated by redundancy-based blocking techniques, removing superfluous comparisons. Existing meta-blocking approaches rely exclusively on schema-agnostic features. In this paper, we demonstrate how “loose” schema information (i.e., statistics collected directly from the data) can be exploited to enhance the quality of the blocks in a holistic loosely schema-aware (meta-)blocking approach that can be used to speed up your favorite Entity Resolution algorithm. We call it Blast (Blocking with Loosely-Aware Schema Techniques). We show how Blast can automatically extract this loose information by adopting a LSH-based step for e ciently scaling to large datasets. We experimentally demonstrate, on real-world datasets, how Blast outperforms the state-of-the-art unsupervised meta-blocking approaches, and, in many cases, also the supervised one