The Wound-Retire concurrency control protocol was proposed to reduce contention for hotspots in in-memory databases. It enhances throughput under high-contention scenarios by allowing transactions to release their locks earlier (referred to as Retire ), thereby reducing the wait times for other transactions. However, the proactive early release of locks introduces additional overhead, making it less efficient than other lock-based protocols in low-contention scenarios. Moreover, the wound strategy it adopts, while effective at preventing deadlocks, may lead to unnecessary transaction aborts. To address these issues, this paper proposes the Rebirth-Retire concurrency control protocol as an enhancement to the Wound-Retire protocol. In this protocol, a lock is retired by a younger transaction that requests it, which reduces unnecessary retire costs in low-contention scenarios. Additionally, rather than aborting younger transactions, older transactions are assigned larger times-tamps (referred to as Rebirth ), unless doing so would result in a deadlock. Experimental evaluations demonstrate that the Rebirth-Retire protocol achieves better throughput and lower abort rate than the Wound-Retire protocol across varying levels of contention workloads.

Edge computing, as an emerging computing paradigm, aims to reduce network bandwidth transmission overhead while storing and processing data on edge nodes. However, the storage strategies required for edge nodes are different from those for existing data centers. Erasure code (EC) strategies have been applied in some decentralized storage systems to ensure the privacy and security of data storage. Product-matrix (PM) regenerating codes (RGCs) as a state-of-the-art EC family are designed to minimize the repair bandwidth overhead or minimize the storage overhead. Nevertheless, the high complexity of the PM framework contains more finite-domain multiplication operations than classical ECs, which heavily consumes computational resources at the edge nodes. In this paper, a theoretical derivation of each step of the PM minimum storage regeneration (PM-MSR) and PM minimum bandwidth regeneration (PM-MBR) codes is performed and the XOR complexity over finite fields is analyzed. On this basis, a new construct called product bitmatrix (PB) is designed to reduce the complexity of XOR operations in the PM framework, and two heuristics are used to further reduce the XOR numbers of the PB-MSR and PB-MBR codes, respectively. The evaluation results show that the PB construction significantly reduces the XOR number compared to the PM-MSR, PM-MBR, Reed–Solomon (RS), and Cauchy RS codes while retaining optimal performance and reliability.

The classical threshold secret sharing scheme by Shamir requires high computation complexity. Many fast secret sharing schemes have been proposed to reduce the computation cost. Another problem of perfect secret sharing scheme is the large share size. Ramp sharing schemes were proposed as a solution to reduce the share size with sacrificing secrecy to some extent. This paper proposes a new ramp scheme, which is adapted from the zigzag-decodable erasure codes for data storage systems. The scheme is shown to approach a linear ramp scheme when the secret size grows to infinity. It is conceptually easy to understand, and has low computation cost, since both its encoding and decoding algorithms are based only on the XOR and bitwise-shift operations.

A code is said to possess the combination property if k source packets are mapped into n k packets and any k out of these n packets are able to recover the information of the original k packets. While the class of maximum-distance-separable codes are well known to have this property, its decoding complexity is generally high. For this reason, a new class of codes which can be decoded by the zigzag-decoding algorithm is considered. It has a lower decoding complexity at the expense of extra storage overhead in each parity packet. In this work, a new construction of a zigzag decodable code is proposed. The novelty of this new construction lies in the careful selection of the amount of bit-shift of each source packet in obtaining each parity packet. Besides, an efficient on-the-air repair scheme based on physical-layer network coding is designed.

As the microblogging systems such as Twitter and Sina Weibo become more and more popular in recent years, the requirement for real-time and personalized search over microblogging systems also becomes more important. In general, a user may expect a quick response that also satisfies her personalized requirements. Unfortunately, since there exist a huge number of users and massive updating microblogs in a microblogging system, personalized search on the system becomes a challenging task. In this paper, we design a new search engine containing four modules to infer the topics of microblogs and update the interests of users, build indexes efficiently, return microblogs for a keyword search, and personalize the order of microblogs, respectively. We also conduct a series of experiments on a real dataset to illustrate the effectiveness and efficiency of the proposed methods.

The popularity of internet usage greatly motivates the online advertising activities. Compared to advertising on traditional media, online advertising has rich information as well as necessary techniques to achieve precise user targeting. This rich information includes the search behaviors of a user, such as queries issued, or the ads clicked by the user. For popular websites with large number of active users, ad delivery targeting at individual users puts too much burden on the system. User segmentation is an alternative way to relieve this burden by grouping users of similar interests together, then the ad delivery system targets the user segments to display relevant ads, instead of individual users. Existing user segmentation work either adapts clustering methods without considering the hidden semantics embedded in the data, such as K-means, or treats users as data instance and clusters users indirectly even if the latent semantics is incorporated into the transformed data, such as PLSA or LDA. In this paper, we present a search behavior based latent semantic user segmentation method and validate its effectiveness on new ads. Instead of treating users as data instances, they are used as attributes of user issued queries or clicked ads which are considered to be data instances. LDA is then applied to this data set to directly obtain the user segments. Compared to popular K-means clustering, our approach achieves higher CTR values on new ads, with only simple search information.

A code is said to have the MDS property if it maps K source blocks into N coded blocks, while any K out of the N coded blocks allow recovery of the original K source blocks. A new vector code that has the MDS property is designed. It allows the source blocks to be recovered by using a fast algorithm called ZigZag decoding. It can serve as the Fractional Repetition code, a regenerating code for distributed storage systems. The overall design becomes very simple to implement, as node failure can be handled by uncoded and exact repair while data retrieval can be performed by XOR operations.

Network traffic monitoring have been gaining attentions due to its importance in telecom industry. However, the monitoring systems deployed in telecom operators are usually too slow because of their disk-based processing approach. To address this problem, an online network traffic monitoring system, named SMART, is designed and developed. The system converts different formats of raw Netflow data (Netflow IPv5, IPv7 and IPv9) to user-defined control flows through combination and filtering. It can compute top-k frequent flows with sliding window, detects burst on arbitrary attributes, and presents results visually to users. The system could be used to replace the traditional offline monitoring system used in Shanghai Telecom. In its daily operation, it is shown that the processing speed achieves 30,000 flows per second. The basis of advanced streaming algorithms and design of robust system architecture enable SMART to achieve good performance.

In this demonstration, we present a prototype content-based dissemination broker, called Sonnet, which is built upon structured overlay network. It combines approximate filtering of XML packets with routing in the overlay network. Deliberate optimization technologies are implemented. The running and tracing of the system in a real-life application are to be demonstrated.

The filtering of XML data is the basis of many complex applications. Lots of algorithms have been proposed to solve this problem. One important challenge is that the number of path queries is huge. It is necessary to take an efficient data structure representing path queries. Another challenge is that these path queries usually vary with time. The maintenance of path queries determines the flexibility and capacity of a filtering system. In this paper, we introduce a novel approximate method for XML data filtering, which uses Bloom filters representing path queries. In this method, millions of path queries can be stored efficiently At the same time, it is easy to deal with the change of these path queries. To improve the filtering performance, we introduce a new data structure, Prefix Filters, to decrease the number of candidate paths. Experiments show that our Bloom filter-based method takes less time to build routing table than automaton-based method. And our method has a good performance with acceptable false positive when filtering XML packets of relatively small depth with millions of path queries.