Ontology type: schema:Chapter Open Access: True
2004
AUTHORSMohamed G. Elfeky , Walid G. Aref , Ahmed K. Elmagarmid
ABSTRACTThe mining of periodic patterns in time series databases is an interesting data mining problem that can be envisioned as a tool for forecasting and predicting the future behavior of time series data. Existing periodic patterns mining algorithms either assume that the periodic rate (or simply the period) is user-specified, or try to detect potential values for the period in a separate phase. The former assumption is a considerable disadvantage, especially in time series databases where the period is not known a priori. The latter approach results in a multi-pass algorithm, which on the other hand is to be avoided in online environments (e.g., data streams). In this paper, we develop an algorithm that mines periodic patterns in time series databases with unknown or obscure periods such that discovering the period is part of the mining process. Based on convolution, our algorithm requires only one pass over a time series of length n, with O(n log n) time complexity. More... »
PAGES605-620
Advances in Database Technology - EDBT 2004
ISBN
978-3-540-21200-3
978-3-540-24741-8
http://scigraph.springernature.com/pub.10.1007/978-3-540-24741-8_35
DOIhttp://dx.doi.org/10.1007/978-3-540-24741-8_35
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1030694382
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Artificial Intelligence and Image Processing",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Information and Computing Sciences",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Purdue University",
"id": "https://www.grid.ac/institutes/grid.169077.e",
"name": [
"Department of Computer Sciences, Purdue University"
],
"type": "Organization"
},
"familyName": "Elfeky",
"givenName": "Mohamed G.",
"id": "sg:person.013215427230.76",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013215427230.76"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Purdue University",
"id": "https://www.grid.ac/institutes/grid.169077.e",
"name": [
"Department of Computer Sciences, Purdue University"
],
"type": "Organization"
},
"familyName": "Aref",
"givenName": "Walid G.",
"id": "sg:person.01326230264.61",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01326230264.61"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Purdue University",
"id": "https://www.grid.ac/institutes/grid.169077.e",
"name": [
"Department of Computer Sciences, Purdue University"
],
"type": "Organization"
},
"familyName": "Elmagarmid",
"givenName": "Ahmed K.",
"id": "sg:person.0717524353.09",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0717524353.09"
],
"type": "Person"
}
],
"citation": [
{
"id": "https://doi.org/10.1145/775047.775109",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1025571209"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/047134608x.w4308",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1030129704"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/047134608x.w4308",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1030129704"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1145/384192.384193",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1040182656"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1145/775047.775128",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1041873341"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1145/347090.347150",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1048020249"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bfb0014140",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1050497818",
"https://doi.org/10.1007/bfb0014140"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1063/1.1531823",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1057717106"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/69.683754",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061213669"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tkde.2003.1262186",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061661232"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1137/0216067",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1062842013"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/icde.1998.655804",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1093929625"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/icde.1995.380415",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1094007712"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/icde.1999.754913",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1094268582"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/icde.2001.914829",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1094936225"
],
"type": "CreativeWork"
}
],
"datePublished": "2004",
"datePublishedReg": "2004-01-01",
"description": "The mining of periodic patterns in time series databases is an interesting data mining problem that can be envisioned as a tool for forecasting and predicting the future behavior of time series data. Existing periodic patterns mining algorithms either assume that the periodic rate (or simply the period) is user-specified, or try to detect potential values for the period in a separate phase. The former assumption is a considerable disadvantage, especially in time series databases where the period is not known a priori. The latter approach results in a multi-pass algorithm, which on the other hand is to be avoided in online environments (e.g., data streams). In this paper, we develop an algorithm that mines periodic patterns in time series databases with unknown or obscure periods such that discovering the period is part of the mining process. Based on convolution, our algorithm requires only one pass over a time series of length n, with O(n log n) time complexity.",
"editor": [
{
"familyName": "Bertino",
"givenName": "Elisa",
"type": "Person"
},
{
"familyName": "Christodoulakis",
"givenName": "Stavros",
"type": "Person"
},
{
"familyName": "Plexousakis",
"givenName": "Dimitris",
"type": "Person"
},
{
"familyName": "Christophides",
"givenName": "Vassilis",
"type": "Person"
},
{
"familyName": "Koubarakis",
"givenName": "Manolis",
"type": "Person"
},
{
"familyName": "B\u00f6hm",
"givenName": "Klemens",
"type": "Person"
},
{
"familyName": "Ferrari",
"givenName": "Elena",
"type": "Person"
}
],
"genre": "chapter",
"id": "sg:pub.10.1007/978-3-540-24741-8_35",
"inLanguage": [
"en"
],
"isAccessibleForFree": true,
"isPartOf": {
"isbn": [
"978-3-540-21200-3",
"978-3-540-24741-8"
],
"name": "Advances in Database Technology - EDBT 2004",
"type": "Book"
},
"name": "Using Convolution to Mine Obscure Periodic Patterns in One Pass",
"pagination": "605-620",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1030694382"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/978-3-540-24741-8_35"
]
},
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"923965c3010c08bf7ba7995e6dab601b23e9bad8479ad6aa9cfddb018cb434e9"
]
}
],
"publisher": {
"location": "Berlin, Heidelberg",
"name": "Springer Berlin Heidelberg",
"type": "Organisation"
},
"sameAs": [
"https://doi.org/10.1007/978-3-540-24741-8_35",
"https://app.dimensions.ai/details/publication/pub.1030694382"
],
"sdDataset": "chapters",
"sdDatePublished": "2019-04-16T08:09",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000360_0000000360/records_118332_00000000.jsonl",
"type": "Chapter",
"url": "https://link.springer.com/10.1007%2F978-3-540-24741-8_35"
}
]
Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-24741-8_35'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-24741-8_35'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-24741-8_35'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-24741-8_35'
This table displays all metadata directly associated to this object as RDF triples.
152 TRIPLES
23 PREDICATES
41 URIs
20 LITERALS
8 BLANK NODES