### Abstract

Data items that arrive online as streams typically have attributes which take values from one or more hierarchies (time and geographic location, source and destination IP addresses, etc.). Providing an aggregate view of such data is important for summarization, visualization, and analysis. We develop an aggregate view based on certain organized sets of large-valued regions (heavy hitters) corresponding to hierarchically discounted frequency counts. We formally define the notion of hierarchical heavy hitters (HHHs). We first consider computing (approximate) HHHs over a data stream drawn from a single hierarchical attribute. We formalize the problem and give deterministic algorithms to find them in a single pass over the input. In order to analyze a wider range of realistic data streams (e.g., from IP traffic-monitoring applications), we generalize this problem to multiple dimensions. Here, the semantics of HHHs are more complex, since a child node can have multiple parent nodes. We present online algorithms that find approximate HHHs in one pass, with provable accuracy guarantees. The product of hierarchical dimensions forms a mathematical lattice structure. Our algorithms exploit this structure, and so are able to track approximate HHHs using only a small, fixed number of statistics per stored item, regardless of the number of dimensions. We show experimentally, using real data, that our proposed algorithms yields outputs which are very similar (virtually identical, in many cases) to offline computations of the exact solutions, whereas straightforward heavy-hitters-based approaches give significantly inferior answer quality. Furthermore, the proposed algorithms result in an order of magnitude savings in data structure size while performing competitively.

Original language | English (US) |
---|---|

Article number | 16 |

Journal | ACM Transactions on Knowledge Discovery from Data |

Volume | 1 |

Issue number | 4 |

DOIs | |

State | Published - Jan 1 2008 |

### Fingerprint

### Keywords

- Approximation algorithms
- Data mining
- Network data analysis

### ASJC Scopus subject areas

- Computer Science(all)

### Cite this

*ACM Transactions on Knowledge Discovery from Data*,

*1*(4), [16]. https://doi.org/10.1145/1324172.1324174

**Finding hierarchical heavy hitters in streaming data.** / Cormode, Graham; Korn, Flip; Muthukrishnan, Shanmugavelayutham; Srivastava, Divesh.

Research output: Contribution to journal › Article

*ACM Transactions on Knowledge Discovery from Data*, vol. 1, no. 4, 16. https://doi.org/10.1145/1324172.1324174

}

TY - JOUR

T1 - Finding hierarchical heavy hitters in streaming data

AU - Cormode, Graham

AU - Korn, Flip

AU - Muthukrishnan, Shanmugavelayutham

AU - Srivastava, Divesh

PY - 2008/1/1

Y1 - 2008/1/1

N2 - Data items that arrive online as streams typically have attributes which take values from one or more hierarchies (time and geographic location, source and destination IP addresses, etc.). Providing an aggregate view of such data is important for summarization, visualization, and analysis. We develop an aggregate view based on certain organized sets of large-valued regions (heavy hitters) corresponding to hierarchically discounted frequency counts. We formally define the notion of hierarchical heavy hitters (HHHs). We first consider computing (approximate) HHHs over a data stream drawn from a single hierarchical attribute. We formalize the problem and give deterministic algorithms to find them in a single pass over the input. In order to analyze a wider range of realistic data streams (e.g., from IP traffic-monitoring applications), we generalize this problem to multiple dimensions. Here, the semantics of HHHs are more complex, since a child node can have multiple parent nodes. We present online algorithms that find approximate HHHs in one pass, with provable accuracy guarantees. The product of hierarchical dimensions forms a mathematical lattice structure. Our algorithms exploit this structure, and so are able to track approximate HHHs using only a small, fixed number of statistics per stored item, regardless of the number of dimensions. We show experimentally, using real data, that our proposed algorithms yields outputs which are very similar (virtually identical, in many cases) to offline computations of the exact solutions, whereas straightforward heavy-hitters-based approaches give significantly inferior answer quality. Furthermore, the proposed algorithms result in an order of magnitude savings in data structure size while performing competitively.

AB - Data items that arrive online as streams typically have attributes which take values from one or more hierarchies (time and geographic location, source and destination IP addresses, etc.). Providing an aggregate view of such data is important for summarization, visualization, and analysis. We develop an aggregate view based on certain organized sets of large-valued regions (heavy hitters) corresponding to hierarchically discounted frequency counts. We formally define the notion of hierarchical heavy hitters (HHHs). We first consider computing (approximate) HHHs over a data stream drawn from a single hierarchical attribute. We formalize the problem and give deterministic algorithms to find them in a single pass over the input. In order to analyze a wider range of realistic data streams (e.g., from IP traffic-monitoring applications), we generalize this problem to multiple dimensions. Here, the semantics of HHHs are more complex, since a child node can have multiple parent nodes. We present online algorithms that find approximate HHHs in one pass, with provable accuracy guarantees. The product of hierarchical dimensions forms a mathematical lattice structure. Our algorithms exploit this structure, and so are able to track approximate HHHs using only a small, fixed number of statistics per stored item, regardless of the number of dimensions. We show experimentally, using real data, that our proposed algorithms yields outputs which are very similar (virtually identical, in many cases) to offline computations of the exact solutions, whereas straightforward heavy-hitters-based approaches give significantly inferior answer quality. Furthermore, the proposed algorithms result in an order of magnitude savings in data structure size while performing competitively.

KW - Approximation algorithms

KW - Data mining

KW - Network data analysis

UR - http://www.scopus.com/inward/record.url?scp=39149089260&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=39149089260&partnerID=8YFLogxK

U2 - 10.1145/1324172.1324174

DO - 10.1145/1324172.1324174

M3 - Article

VL - 1

JO - ACM Transactions on Knowledge Discovery from Data

JF - ACM Transactions on Knowledge Discovery from Data

SN - 1556-4681

IS - 4

M1 - 16

ER -