Abstract Graph is a generic model of various networks in real-world applications. And, graph embedding aims to represent nodes (edges or graphs) as low-dimensional vectors which can be fed into machine learning algorithms for downstream graph analysis tasks. However, existing random walk-based node embedding methods often map some nodes with (dis)similar local structures to (near) far vectors. To overcome this issue, this paper proposes to implement node embedding by constructing a context graph via a new defined ripple distance over ripple vectors, whose components are the hitting times of fully condensed neighborhoods and thus characterize their structures as pure quantities. The distance is able to capture the (dis)similarities of nodes’ local neighborhood structures and satisfies the triangular inequality. The neighbors of each node in the context graph are defined via the ripple distance, which makes the short random walks from a given node over the context graph only visit its similar nodes in the original graph. This property guarantees that the proposed method, named as $$\mathsf {ripple2vec}$$ ripple2vec , is able to map (dis)similar nodes to (far) near vectors. Experimental results on real datasets, where labels are mainly related to nodes’ local structures, show that the results of $$\mathsf {ripple2vec}$$ ripple2vec behave better than those of state-of-the-art methods, in node clustering and node classification, and are competitive to other methods in link prediction.

Data transmissions suffer from TCP’s poor performance since the introduction of the first commercial wireless services in the 1990s. Recent years have witnessed a surge of academia and industry activities in the field of TCP performance optimization. For a TCP flow whose last hop is a wireless link, congestions in the last hop dominate its performance. We implement an integral data sampling, network monitoring, and rate control software-defined wireless networking (SDWN) system. By analysing our sampled data, we find that there exist strong relationships between congestion packet loss behaviors and the instant cross-layer network metric measurements (states). We utilize these qualitative relationships to predict future congestions in wireless links and enhance TCP performance by launch necessary rate control locally on the access points (AP) before the congestions. We also implement modeling and rate control modules on this platform. Our platform senses the instant wireless dynamic and takes actions promptly to avoid future congestions. We conduct real-world experiments to evaluate its performance. The experiment results show that our methods outperform the bottleneck bandwidth and RTT (BBR) protocol and a recently proposed protocol Vivace on throughput, delay, and jitter performance at least 16.5%, 25%, and 12.6%, respectively.

Abstract This paper revisits set containment join (SCJ) problem, which uses the subset relationship (i.e., $$\subseteq$$ ⊆ ) as condition to join set-valued attributes of two relations and has many fundamental applications in commercial and scientific fields. Existing in-memory algorithms for SCJ are either signature-based or prefix-tree-based. The former incurs high CPU cost because of the enumeration of signatures, while the latter incurs high space cost because of the storage of prefix trees. This paper proposes a new adaptive parameter-free in-memory algorithm, named as frequency-hashjoin or $${\mathsf {FreshJoin}}$$ FreshJoin in short, to evaluate SCJ efficiently. $${\mathsf {FreshJoin}}$$ FreshJoin builds a flat index on-the-fly to record three kinds of signatures (i.e., two least frequent elements and a hash signature whose length is determined adaptively by the frequencies of elements in the universe set). The index consists of two sparse inverted indices and two arrays which record hash signatures of all sets in each relation. The index is well organized such that $${\mathsf {FreshJoin}}$$ FreshJoin can avoid enumerating hash signatures. The rationality of this design is explained. And, the time and space cost of the proposed algorithm, which provide a rule to choose $${\mathsf {FreshJoin}}$$ FreshJoin from existing algorithms, are analyzed. Experiments on 16 real-life datasets show that $${\mathsf {FreshJoin}}$$ FreshJoin usually reduces more than 50% of space cost while remains as competitive as the state-of-the-art algorithms in running time.

With the wide application of location-based social networks (LBSNs), point-of-interest (POI) recommendation has become one of the major services in LBSNs. The behaviors of users in LBSNs are mainly checking in POIs, and these checking-in behaviors are influenced by user's behavior habits and his/her friends. In social networks, social influence is often used to help businesses to attract more users. Each target user has a different influence on different POI in social networks. This paper selects the list of POIs with the greatest influence for recommending users. Our goals are to satisfy the target user's service need, and simultaneously to promote businesses' locations (POIs). This paper defines a POI recommendation problem for location promotion. Additionally, we use submodular properties to solve the optimization problem. At last, this paper conducted a comprehensive performance evaluation for our method using two real LBSN datasets. Experimental results show that our proposed method achieves significantly superior POI recommendations comparing with other state-of-the-art recommendation approaches in terms of location promotion.

In this work, we focus on minimizing the overall network energy consumption problem under delay-constrained: N different packets from N time varying channels must be transmitted by a hard deadline of T slots. Each transmitter determines how much power to transmit with, during each time slot based on the current channel quality and the number of un transmitted bits, with the objective of minimizing overall network energy consumption. We transform the non-convex optimization problem into a geometric programming problem which has convex form and propose a distributed approximate optimization algorithm. Moreover, a lazy updating distributed algorithm is also presented for infrequent message passing. Experimental results show that the proposed distributed algorithm converges fast and the results of the distributed algorithm and results of centralized algorithm are very close.

We present the design, implementation, and evaluation of AirCloud -- a novel client-cloud system for pervasive and personal air-quality monitoring at low cost. At the frontend, we create two types of Internet-connected particulate matter (PM2:5) monitors -- AQM and miniAQM, with carefully designed mechanical structures for optimal air-flow. On the cloud-side, we create an air-quality analytics engine that learn and create models of air-quality based on a fusion of sensor data. This engine is used to calibrate AQMs and mini-AQMs in real-time, and infer PM2:5 concentrations. We evaluate AirCloud using 5 months of data and 2 month of continuous deployment, and show that AirCloud is able to achieve good accuracies at much lower cost than previous solutions. We also show three real applications built on top of AirCloud by 3rd party developers to further demonstrate the value of our system.

The abundance of Web 2.0 resources in various media formats calls for better resource integration to enrich user experience. This naturally leads to a new cross-modal resource search requirement, in which a query is a resource in one modal and the results are closely related resources in other modalities. With cross-modal search, we can better exploit existing resources. Tags associated with Web 2.0 resources are intuitive medium to link resources with different modality together. However, tagging is by nature an ad hoc activity. They often contain noises and are affected by the subjective inclination of the tagger. Consequently, linking resources simply by tags will not be reliable. In this paper, we propose an approach for linking tagged resources to concepts extracted from Wikipedia, which has become a fairly reliable reference over the last few years. Compared to the tags, the concepts are therefore of higher quality. We develop effective methods for cross-modal search based on the concepts associated with resources. Extensive experiments were conducted, and the results show that our solution achieves good performance.

Traffic routing guidance algorithms which only consider user satisfaction will result in road density unbalance. On the contrary, the route algorithms will not meet user request if they merely focus on the traffic load balance. So it is important to take user satisfaction and traffic load into consideration at the same time. Although a few works focus on this combination, they ignore the difference between the user requests. We propose a traffic dispersion routing algorithm on VANET naming BPR-US which is based on backpressure theory. It tries to satisfy the user demand and also separates the traffic flow. At last, we make simulation experiments to compare BPR-US algorithm and other route guidance algorithms, proving that BPR-US algorithm can have a better effect on both user satisfaction and traffic load.

In corporate networks, daily business data are generated in gigabytes or even terabytes. It is costly to process aggregate queries in those systems. In this paper, we propose PACA, a probably approximately correct aggregate query processing scheme, for answering aggregate queries in structured Peer-to-Peer (P2P) network. PACA retrieves random samples from peers' databases and applies the samples to process queries. Instead of scanning the entire database of each peer, PACA only accesses a small random number of data. Moreover, based on the query distribution,PACA publishes a precomputed synopsis and uses the synopsis to answer future queries. Most queries are expected to be answered by the precomputed synopsis partially or fully. And the synopsis is adaptively tuned to follow the query distribution. Experiments on the PlanetLab show the effectiveness of the approach.

In many decision making applications, users typically issue aggregate queries. To evaluate these computationally expensive queries, online aggregation has been developed to provide approximate answers (with their respective confidence intervals) quickly, and to continuously refine the answers. In this paper, we extend the online aggregation technique to a distributed context where sites are maintained in a DHT (Distributed Hash Table) network. Our Distributed Online Aggregation (DoA) scheme iteratively and progressively produces approximate aggregate answers as follows: in each iteration, a small set of random samples are retrieved from the data sites and distributed to the processing sites; at each processing site, a local aggregate is computed based on the allocated samples; at a coordinator site, these local aggregates are combined into a global aggregate. DoA adaptively grows the number of processing nodes as the sample size increases. To further reduce the sampling overhead, the samples are retained as a precomputed synopsis over the network to be used for processing future queries. We also study how these synopsis can be maintained incrementally. We have conducted extensive experiments on PlanetLab. The results show that our DoA scheme reduces the initial waiting time significantly and provides high quality approximate answers with running confidence intervals progressively.

Content-based retrieval of spatio-temporal patterns from human motion databases is inherently nontrivial since finding effective distance measures for such data is difficult. These data are typically modelled as time series of high dimensional vectors which incur expensive storage and retrieval cost as a result of the high dimensionality. In this paper, we abstract such complex spatio-temporal data as a set of frames which are then represented as high dimensional categorical feature vectors. New distance measures and queries for high dimensional categorical time series are then proposed and efficient query processing techniques for answering these queries are developed. We conducted experiments using our proposed distance measures and queries on human motion capture databases. The results indicate that significant improvement on the efficiency of query processing of categorical time series (more than 10,000 times faster than that of the original motion sequences) can be achieved while guaranteeing the effectiveness of the search.