Operator Placement for Snapshot Multi-predicate Queries in Wireless Sensor Networks

Chatzimilioudis, Georgios; Hakkoymaz, Huseyin; Mamoulis, Nikos; Gunopulos, Dimitrios

doi:10.1109/mdm.2009.13

Cited by 17 publications

(11 citation statements)

References 29 publications

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“…The exponent of y is designed in such a way, that nodes in nearby classes have higher probability of being connected, than nodes that are far apart. We choose to experiment with the combinations (x, y) = (1,4) and (x, y) = (3,4). For x y = 1 4 we generate a graph with 1026 nodes and 32427 edges.…”

Section: Results Using Synthetic Datasetsmentioning

confidence: 99%

“…The resulting subgraph is intended to portray the propagation of an original concept or finding in this paper through the physics community: a filter in this setting can be seen as an opportune point in the knowledge transfer process to purge potentially redundant citations of the primary source (e.g., derivative work). 4 The resulting citation graph G Citation is acyclic and contains 9,982 nodes and 36,070 edges. As for G Phrase and the Twitter graph, G Citation has a power-law distribution of in and out degrees.…”

Section: Results Using Synthetic Datasetsmentioning

confidence: 99%

“…Coordinated communication of sensory data is exemplified by recent work that went beyond standard aggregation and focused on minimizing the communication cost required for the evaluation of multi-predicate queries scheduled across network nodes [4]. In that work, a dynamic-programming algorithm is used to determine an optimal execution order of queries.…”

Section: Centrality In Networkmentioning

confidence: 99%

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The filter-placement problem and its application to minimizing information multiplicity

et al. 2012

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In many information networks, data items -such as updates in social networks, news flowing through interconnected RSS feeds and blogs, measurements in sensor networks, route updates in ad-hoc networks -propagate in an uncoordinated manner: nodes often relay information they receive to neighbors, independent of whether or not these neighbors received the same information from other sources. This uncoordinated data dissemination may result in significant, yet unnecessary communication and processing overheads, ultimately reducing the utility of information networks. To alleviate the negative impacts of this information multiplicity phenomenon, we propose that a subset of nodes (selected at key positions in the network) carry out additional information filtering functionality. Thus, nodes are responsible for the removal (or significant reduction) of the redundant data items relayed through them. We refer to such nodes as filters. We formally define the Filter Placement problem as a combinatorial optimization problem, and study its computational complexity for different types of graphs. We also present polynomial-time approximation algorithms and scalable heuristics for the problem. Our experimental results, which we obtained through extensive simulations on synthetic and real-world information flow networks, suggest that in many settings a relatively small number of filters is fairly effective in removing a large fraction of redundant information.

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Section: Results Using Synthetic Datasetsmentioning

confidence: 99%

Section: Results Using Synthetic Datasetsmentioning

confidence: 99%

Section: Centrality In Networkmentioning

confidence: 99%

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The filter-placement problem and its application to minimizing information multiplicity

et al. 2012

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“…The unconstrained optimization phase is performed in a centralized manner, while the constrained optimization phase is performed in a distributed manner. [38] X Pietzuch et al [40] X Bonfils et al [9] X Chatzimilioudis et al [11] X Chatzimilioudis et al [13] X Chatzimilioudis et al [12] X F. Starks et al [45] X…”

Section: Constrained Optimizationmentioning

confidence: 99%

Mobile Distributed Complex Event Processing—Ubi Sumus? Quo Vadimus?

Starks

Goebel

Kristiansen

et al. 2017

Lecture Notes on Data Engineering and Communications Technologies

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One important class of applications for the Internet of Things is related to the need to gain timely and continuous situational awareness, like smart cities, automated traffic control, or emergency and rescue operations. Events happening in the real-world need to be detected in real-time based on sensor data and other data sources. Complex Event Processing (CEP) is a technology to detect complex (or composite) events in data streams and has been successfully applied in high volume and high velocity applications like stock market analysis. However, these application domains faced only the challenge of high performance, while the Internet of Things and Mobile Big Data introduce a new set of challenges caused by mobility. This chapter aims to explain these challenges and to give an overview on how they are solved respectively how far state-of-the-art research has advanced to be useful to solve Mobile Big Data problems. At the infrastructure level the main challenge is to trade performance against resource consumption and energy efficiency and operator placement is the most dominant mechanism to address these problems. At the application and consumer level, mobile queries pose a new set of challenges for CEP related to continuously changing positions of consumers and data sources, and the need to adapt the query processing to these changes. Finally, proper methods and tools for systematical testing and reproducible performance evaluation for mobile distributed CEP are needed but not yet available.

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“…We use the algorithm proposed in Chatzimilioudis et al [5] for finding an initial operator placement. We exploit the mandatory query dissemination to collect some information about the network with minimum overhead.…”

Section: Initial Operator Placementmentioning

confidence: 99%

A Distributed Technique for Dynamic Operator Placement in Wireless Sensor Networks

Chatzimilioudis

Mamoulis²,

Gunopulos

2010

2010 Eleventh International Conference on Mobile Data Management

Self Cite

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Abstract-We present an optimal distributed algorithm to adapt the placement of a single operator in high communication cost networks, such as a wireless sensor network. Our parameterfree algorithm finds the optimal node to host the operator with minimum communication cost overhead. Three techniques, proposed here, make this feature possible: 1) identifying the special, and most frequent case, where no flooding is needed, otherwise 2) limitation of the neighborhood to be flooded and 3) variable speed flooding and eves-dropping. When no flooding is needed the communication cost overhead for adapting the operator placement is negligible. In addition, our algorithm does not require any extra communication cost while the query is executed. In our experiments we show that for the rest of cases our algorithm saves 30%-85% of the energy compared to previously proposed techniques. To our knowledge this is the first optimal and distributed algorithm to solve the 1-median (Fermat node) problem. I. OVERVIEW AND MOTIVATIONNetwork applications often need to perform in-network query processing. Sensor networks are being deployed in the physical or urban environment to benefit scientific research or security surveillance. An example of a query in a network, which is monitoring traffic in a busy downtown area, could be "How many cars took the same route of passing through intersections A, B and C?". To avoid the cost of communicating all the data lists from the nodes in regions A, B and C to the querying node, the query must be executed in-network. Data lists generated on the source nodes are fed into operators on intermediate nodes that combine several lists from different sources. The amount of data is reduced due to the selectivity of the operators and the data that reaches the querying node is the final answer.An operator, that is involved in the in-network processing, can be placed on a node of the network. It takes in elements from source nodes, processes them, and sends the output to either another operator node or to the sink. Shipping elements over an edge in the graph imposes a cost that is dependent on the weight of the elements. Therefore, the placement of an operator can greatly affect the cost of answering a query since it affects the number of edges the elements have to travel over and the weight of the elements, since usually the output weight is not the sum of the input weights. It is typical to have continuous queries that require an answer over a continuous period of epochs. In most applications the sources and operators are not producing the same weight of elements in every epoch. Similarly, nodes in the network might be mobile resulting in different hop-distances between nodes in every epoch. Therefore, the initial operator placement might not be good enough for future epochs. It is a large overhead to re-run the algorithms for finding a good placement for the operators of the query. Instead, the technique followed in literature is to update the placement of just the operators that are affected by the weight...

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Operator Placement for Snapshot Multi-predicate Queries in Wireless Sensor Networks

Cited by 17 publications

References 29 publications

The filter-placement problem and its application to minimizing information multiplicity

The filter-placement problem and its application to minimizing information multiplicity

Mobile Distributed Complex Event Processing—Ubi Sumus? Quo Vadimus?

A Distributed Technique for Dynamic Operator Placement in Wireless Sensor Networks

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