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One of the most popular applications of Location Based Services (LBS) is recommending a Point of Interest (POI) based on user's preferences and geolocations. However, the existing approaches have not tackled the problem of jointly determining: (a) a sequence of POIs that can be traversed within certain budget (i.e., limit on distance) and simultaneously provide a highenough diversity; and (b) recommend the best origin (i.e., the hotel) for a given user, so that the desired route of POIs can be traversed within the specified constraints. In this work, we take a first step towards identifying this new problem and formalizing it as a novel type of a query. Subsequently, we present naïve solutions and experimental observations over a reallife datasets, illustrating the tradeoffs in terms of (dis)associating the initial location from the rest of the POIs.

When learning from streaming data, a change in the data distribution, also known as concept drift, can render a previouslylearned model inaccurate and require training a new model. We present an adaptive learning algorithm that extends previous driftdetectionbased methods by incorporating drift detection into a broader stablestate/reactivestate process. The advantage of our approach is that we can use aggressive drift detection in the stable state to achieve a high detection rate, but mitigate the false positive rate of standalone drift detection via a reactive state that reacts quickly to true drifts while eliminating most false positives. The algorithm is generic in its base learner and can be applied across a variety of supervised learning problems. Our theoretical analysis shows that the risk of the algorithm is (i) statistically better than standalone drift detection and (ii) competitive to an algorithm with oracle knowledge of when (abrupt) drifts occur. Experiments on synthetic and real datasets with concept drifts confirm our theoretical analysis.

Quasicliques are dense incomplete subgraphs of a graph that generalize the notion of cliques. Enumerating quasicliques from a graph is a robust way to detect densely connected structures with applications in bioinformatics and social network analysis. However, enumerating quasicliques in a graph is a challenging problem, even harder than the problem of enumerating cliques. We consider the enumeration of top k degreebased quasicliques and make the following contributions: (1) we show that even the problem of detecting whether a given quasiclique is maximal (i.e., not contained within another quasiclique) is NPhard. (2) We present a novel heuristic algorithm K ernel QC to enumerate the k largest quasicliques in a graph. Our method is based on identifying kernels of extremely dense subgraphs within a graph, followed by growing subgraphs around these kernels, to arrive at quasicliques with the required densities. (3) Experimental results show that our algorithm accurately enumerates quasicliques from a graph, is much faster than current stateoftheart methods for quasiclique enumeration (often more than three orders of magnitude faster), and can scale to larger graphs than current methods.

When learning from streaming data, a change in the data distribution, also known as concept drift, can render a previouslylearned model inaccurate and require training a new model. We present an adaptive learning algorithm that extends previous driftdetectionbased methods by incorporating drift detection into a broader stablestate/reactivestate process. The advantage of our approach is that we can use aggressive drift detection in the stable state to achieve a high detection rate, but mitigate the false positive rate of standalone drift detection via a reactive state that reacts quickly to true drifts while eliminating most false positives. The algorithm is generic in its base learner and can be applied across a variety of supervised learning problems. Our theoretical analysis shows that the risk of the algorithm is competitive to an algorithm with oracle knowledge of when (abrupt) drifts occur. Experiments on synthetic and real datasets with concept drifts confirm our theoretical analysis.

We present shared memory parallel algorithms for maximal biclique enumeration (MBE), the task of enumerating all complete dense subgraphs (maximal bicliques) from a bipartite graph, which is widely used in the analysis of social, biological, and transactional networks. Since MBE is computationally expensive, it is necessary to use parallel computing to scale to large graphs. Our parallel algorithm ParMBE efficiently uses the power of multiple cores that share memory. From a theoretical view, ParMBE is workefficient with respect to a stateoftheart sequential algorithm. Our experimental evaluation shows that ParMBE scales well up to 64 cores, and is significantly faster than current parallel algorithms. Since ParMBE was yielding a superlinear speedup compared to the sequential algorithm on which it was based (MineLMBC), we develop an improved sequential algorithm FMBE, through "sequentializing" ParMBE.

We consider messageefficient continuous random sampling from a distributed stream, where the probability of inclusion of an item in the sample is proportional to a weight associated with the item. The unweighted version, where all weights are equal, is well studied, and admits tight upper and lower bounds on message complexity. For weighted sampling with replacement, there is a simple reduction to unweighted sampling with replacement. However, in many applications the stream may have only a few heavy items which may dominate a random sample when chosen with replacement. Weighted sampling without replacement (weighted SWOR) eludes this issue, since such heavy items can be sampled at most once. In this work, we present the first messageoptimal algorithm for weighted SWOR from a distributed stream. Our algorithm also has optimal space and time complexity. As an application of our algorithm for weighted SWOR, we derive the first distributed streaming algorithms for tracking heavy hitters with residual error. Here the goal is to identify stream items that contribute significantly to the residual stream, once the heaviest items are removed. Residual heavy hitters generalize the notion of $\ell_1$ heavy hitters and are important in streams that have a skewed distribution of weights. Inmore »

Stratified random sampling (SRS) is a widely used sampling technique for approximate query processing. We consider SRS on continuously arriving data streams, and make the following contributions. We present a lower bound that shows that any streaming algorithm for SRS must have (in the worst case) a variance that is Ω(r) factor away from the optimal, where r is the number of strata. We present SVOILA, a streaming algorithm for SRS that is locally varianceoptimal. Results from experiments on real and synthetic data show that SVOILA results in a variance that is typically close to an optimal offline algorithm, which was given the entire input beforehand. We also present a varianceoptimal offline algorithm VOILA for stratified random sampling. VOILA is a strict generalization of the wellknown Neyman allocation, which is optimal only under the assumption that each stratum is abundant, i.e. has a large number of data points to choose from. Experiments show that VOILA can have significantly smaller variance (1.4x to 50x) than Neyman allocation on realworld data.